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Technology's daily show (formerly the Technology Brothers Podcast). Streaming live on X and YouTube from 11 - 2 PM PST Monday - Friday. Available on X, Apple, Spotify, and YouTube.
You're watching TVPN.
Speaker 2:Today is Thursday, 11/13/2025. We are live from the TVPN UltraDome, the temple of technology, the fortress of finance.
Speaker 1:The capital of capital.
Speaker 2:Ramp. Time is money. Say both. Easy to use corporate cards. Bill Payne.
Speaker 2:Accounting a whole lot more all in one place. There is a bunch of breaking news. The big news out of OpenAI, of course. The OpenAI show continues. Sarah Fryer had some comments about ChatGPT's growth potentially slowing, and it's unclear.
Speaker 2:It's not in decline. It's maybe deceleration. We'll have to dig into that. It had me thinking about, about debt, and I was thinking about just the fact that the debt has come to to tech for the first time, really. And this was sort of my take, and I'm I'm a little bit this is an area that I know the least about.
Speaker 2:And so I was doing some research learning a different learning about a different industry since it's it's just so abstract to me because I've never worked in private credit or really seen that industry or even just really studied it. You appreciate leverage. You've never been a big I appreciate it. Yeah. And and mostly, I was just wondering, like, we we we keep going back and forth on the on the debt is coming to tech narrative as, like, it's very scary.
Speaker 2:Like, when debt comes, only bad things happen. You know, we we we live through the the global financial crisis, and, there's a lot of jitters when debt is around. It's like, oh, you could get wiped out. You could blow up. The backstop comes in.
Speaker 2:It just feels like all of a sudden we're we're we're talking about things with, like, a much more serious consequence than, oh, yeah. A startup raised some money, and it didn't pan out. And it was a zero, and it wound up being a write down, but it was part of, you know, a portfolio of equities that is averaged out across a whole bunch of different LPs. It like, there's no there's no even when, even when, you know, like Theranos blew up, it was only equity holders that were lost. It wasn't this higher entire industry.
Speaker 2:And so, it wasn't it didn't turn into this, like, systemic issue. Right? Yeah. But now it feels like with the 1,400,000,000,000.0, of, you know, backlog that OpenAI has kind of opened up across a whole bunch of different deals, There is this worry that, you know, maybe the level of indebtedness could be risky. The level of risk in the system, the level of investment in the system, could be something that's bigger than just, oh, if you're in this one name, you're taking a big risk.
Speaker 2:Now it's maybe like, hey. We're all taking a risk, and if we're talking about backstops, at least. Yep. And so I was trying to understand, there's this old phrase in from 2006, coined by Clive Humby, classic coinage. I love a coinage.
Speaker 2:He said data is the new oil. And back then in 2006, his point was, he was working at a data as a data scientist at Tesco, which is this British grocery store. I don't know if you
Speaker 3:know this story. But he was working
Speaker 2:at this British grocery store chain, and his point was we have all this data on a customer has is in the rewards program. We see that they buy a Thanksgiving turkey before Thanksgiving. We see that they buy this type of paper towels or this type of milk or whatever. We have all this data, but we don't really do anything with it. The data is not valuable.
Speaker 2:We need to refine it, much like oil, into gasoline. And once we refine it into gasoline, then we can do things like targeted advertising, and we can increase our customer value. And so it was basically just a generic, a generic, call to action for taking data science seriously, for just don't just have the data there. Understand that the data is valuable if you extract it, if you work on it. But the but the the metaphor people have been saying data data is the new oil for, I guess, two decades now, and it never really sat that well with me because unlike oil, data is not, perfectly fungible.
Speaker 1:Yeah.
Speaker 2:So one one tranche of data is not equivalent to another. Like, Reddit is clearly very valuable since it kinda, you know, provided the backbone for g p t three. The all the analytics data that flows out of some mobile game is
Speaker 1:basically data is worthless. A lot of data is worthless. All oil Yeah. Is has at least some value.
Speaker 2:Essentially. I mean, I guess there are different levels of crude. Right? They're they're different different grades. And I was actually trying to play out the metaphor more, and I was wondering, like, can we get to a place where, you know, so we can ring intelligence out of raw data like the oil, and the result can be low octane gasoline, kinda like midwit, you know, level, like, slop or an AI slop, or it can be jet fuel, like a deep research report that's actually pretty great, or some code that's really reliable and really useful.
Speaker 2:But it all depends on the processing met methodology. But the more interesting interesting data is the new oil take that I don't think was considered in 2006 is that, maybe the tech industry is gonna look like the oil and gas industry soon. Like, I was looking up what how much debt is in the oil and gas industry? It's over a trillion dollars of debt. And it's like, it's fine.
Speaker 2:Like yeah. Exactly. Yeah. Clap. It's fine.
Speaker 2:Like, it's not this, like, huge systemic issue. It was 2,000,000,000,000 a lot like, you know, a decade ago, and then it went down and then went up. And it's like, it's all just a function of, like, how much oil and gas is going on, how many where are the new projects, how big are the projects, how much debt goes in. Like, just having a lot of mortgages in America is not intrinsically risky.
Speaker 1:K. The difference the difference is that if you identify oil in the ground Mhmm. And you figure out how much it's gonna cost you to extract it Mhmm. And how long you think you'll be able like, basically, estimating, like, the how much how much oil is actually available
Speaker 3:Yes.
Speaker 1:This site Yeah. Then you can lend against that pretty predictably because you know that the price of oil is gonna fluctuate. But in general, as long as it's in a in some range, it will be, like, a profitable operation to pull it out of the ground. And I think it's a little bit easier to lend against that than GPUs today when we're the big debate is around depreciation schedules and will these GPUs you know, we we we have a sense that a data center that has power. And and basically a box with a lot of power will be valuable in the future.
Speaker 1:But if you're if a lot of the cost of a new data center is GPUs, it's harder to gauge on what the value of those GPUs will be in in, you know, four years than than it is. Okay. It will this oil, like, production site still be producing oil in five years? I think that's a bit easier to answer and easier to lend against.
Speaker 2:Maybe. I mean, sometimes there are tracks that only produce oil for four years, and you underwrite it against a four year depreciation schedule. And as long as you get the as long as you you match the risk to the reward, the deal pencils out just fine. But but I understand what you're what you're getting at. And I think that as we dig into the OpenAI news, I think we'll have more we can synthesize some of what the of the recent the recent leaks and and rumored statements around, potentially a plateau and demand for tokens on maybe the consumer side.
Speaker 2:But, it it is just like a wildly different question. Like, the fact that you're walking through that math is very different than what the venture capitalists in 2000 were doing. Like, Ev Randall, who's coming on the show, on Friday tomorrow, he always says he goes back to the Google prospectus from when they IPO ed. And Google was, like, the most pure play, just beautiful software business. So Google in from from 2001 to 2004 grew from 86,000,000 in revenue to 3,200,000,000.0 in revenue.
Speaker 2:And net income over that period went from 10,000,000 to 400,000,000, and that includes stock based comp. So they were still making 400,000,000 in profit with the stock based comp. Googlers made a lot of money. They gave away a lot of stock. And so it was it was not it didn't look like an oil business.
Speaker 2:There was not this big CapEx build out. There was not this big r and even this crazy r and d phase. Like, there was just not there was there wasn't that much capital that went into Google before it became this monster cash flow machine. It was just Infinite money It was sort of an infinite money glitch. It was this beautiful algorithm that was just discovered, and it was so elegant, and it just produced this monopoly insane, like, growth rate for so long.
Speaker 2:And then, of course, they've been challenged, and they expanded, and there's million things. And then eventually, CapEx did come into the picture as they grew their cloud, their cloud infrastructure, GCP, all this other stuff. But, but for a long time, like, tech just meant take a bet on a company, and it's either zero or a trillion dollars or something like that. And so it's a lot different. And I wanted to dig into, like, the actual structure of one of these deals because I don't I think that tech people I was I was almost gonna call this like, why is no one talking about Blue Owl?
Speaker 2:Because people obviously on Wall Street are definitely talking about Blue Owl. It's a it's a public company. The stock's, I think, down, like,
Speaker 1:Most of your child.
Speaker 2:This year. But but it's the data center
Speaker 1:of finance. Private credit.
Speaker 2:Yes. Private credit. Exactly. And so I wanted to understand, like, how does BlueOut actually interact with one of these data center deals? Because that's important to understand, like, where the risk winds up living.
Speaker 2:So I'll break one of these down, but first, I'll tell you about Restream. One livestream, 30 plus destinations, multistream, and reach your audience wherever they are. So for Hyperion, you remember the Hyperion release? Zuck went on threads and announced that he was gonna be building a five gigawatt data center. It gonna be as big as Manhattan.
Speaker 1:Looks like somewhat of a Manhattan project.
Speaker 2:Somewhat of a Manhattan project. Exactly. So the the crazy, crazy thing about that deal. So so he spins up the the the he's he puts out the announcement post on on on threads, says, hey. We're going to build this five gigawatt data center campus.
Speaker 2:It's gonna be online in a few years. It's gonna be as big as Manhattan. He and he and he shares some of, like, where it's going to be, how many racks are there gonna be, square footage, stuff like that. But he's basically just announcing that, like, hey. The project's financed.
Speaker 2:We're ready to go on this. Like, you would expect that when that it's a $27,000,000,000 deal. You would expect that, okay, Meta went down. They they spent $27,000,000,000. It's worth it.
Speaker 2:They're gonna no. They got paid 3,000,000,000. They got paid 3,000,000,000. And the reason is because Blue Owl financed it with external debt, and they are basically paying Meta upfront for the right to have them as a as a tenant, as a leaser for a very long time. So they get this, like, we have Meta as a client.
Speaker 2:Meta's always gonna pay their bills. They're not they're like no matter what happens with the AI build out, they're gonna be good for it because they have this cash machine. So they are, like, the best possible tenant. Not like some fly by night, oh, yeah. I'm a start up, but maybe I'll be around in a few years.
Speaker 2:It's like, it's meta. They're gonna pay their bills. And so you have this massive data center project that's going to be paid for. Even if it's not producing any valuable tokens, Zuck's still gonna he's not just gonna default and be like, yeah. Take the company.
Speaker 2:No way. He's gonna pay. And so in exchange for that, they have 3,000,000,000 up front. And so there's just each one of these deals, I think the more you dig into them, I wanna have more of these people on. Mohamed El Arian at PIMCO or was formerly at PIMCO.
Speaker 2:I he I know he can explain this a little bit more. I wanna have more people on the on the show to help us get up to speed on this because this feels deeply important to the current AI build out boom, the tech story. It feels it feels like an entirely new piece of the puzzle to understand where this technology is going, and I don't feel equipped to to understand it at all. Barron's did have a great article about blue, Blue Owl and a very funny interaction between Blue Owl and Jamie Dimon, and they're going at it. And I think it's interesting to read through.
Speaker 2:So let's read through a little bit of this to give you a little bit more flavor on what's going on at BlueOwl because if you're just in tech, if you're just in venture, you might not know that much about them. But first, let me tell you about Privy. Wallet infrastructure for every bank. Privy makes it easy to build on crypto rails, securely spin up white label wallets, sign transactions, integrate on chain infrastructure all through one simple API. So in Barron's, they I had this this article's from, October 24.
Speaker 2:I had it on the table. We never got to it. We're getting to it now. It's title of the article is private asset star Blue Owl has been flying high. Is it too close to the sun?
Speaker 2:This feels like a headline Jordy would write. I'm very skeptical about, about what's going on in the AI build out in the AI boom. But let's let's dig it
Speaker 1:and see how it's private asset star. Yeah. Gonna start calling our friends private asset stars.
Speaker 2:For sure. For sure. So the article says, suddenly, Blue Owl Capital is everywhere this past Tuesday. The Upstart alternative investment firm with an aptitude for private credit announced a financing deal for Meta Platform's $27,000,000,000 AI data center in Louisiana. That is Hyperion that I was mentioning earlier.
Speaker 2:Before the the week before at the PACC AIS Alternative Asset Summit in Los Angeles, Blue Owl's co CEOs, co CEO Mark Lipchitz, called JPMorgan Chase's CEO Jamie Dimon cockroach warning about risk in private credit, an odd kind of fearmongering. So, what happened there was, we talked about that that blow up in the private credit, world, and I have a little bit of background on this. So, where where did he say this? So, I need to actually pull up what happened with the, with the private with the cockroach statement because it's very funny.
Speaker 1:That's the First Brands?
Speaker 2:It's First Brands. Let me see. First Brands. First Brands. So so, basically, private credit has been growing a ton.
Speaker 2:We've talked about this a few times. Ares is massive now. Blue Owl is really big, and, and the whole and and and there's basically been this little bit of a fight emerging between where the debt is coming from. Do you do private credit, or do you go with the traditional bank route? And so Jamie Dimon, at least I'm I'm pretty sure he's going head to head against Blue Owl in a bunch of these deals.
Speaker 2:And so there's this question of, like, you know, there are they are they chirping at each other intentionally? And so, Jamie Dimon, was cautioning investors about potential risks in the credit market by invoking a proverb. When you see one cockroach, there are probably more. And so he was referring to recent loan defaults, such as the bankruptcy of auto parts maker First Brands and subprime lender Tricolor Holdings, as warning signs of broader credit issues. So Jamie noted, Diamond noted that JPMorgan took losses on some bad loans and implied that trouble in one corner of the credit market could mean undiscovered problems elsewhere, implicitly casting doubt on the booming private credit sector.
Speaker 2:And so Mark Lipchitz fires back, and he says, I guess he's saying that there might be a lot more cockroaches at JPMorgan. And so he's he's actually saying, like, oh, yeah. You maybe you should go check out their books and see if they have other, you know, bad stuff because I so First Brands collapsed was an isolated case of alleged fraud, actually, in the syndicated loan market, and it was not in the direct lending arena where Blue Owl operates. So Blue Owl had no exposure to First Brands, and yet and yet, Mark Lipchitz was still, you know, firing back at JPMorgan for kind of casting doubt on the, on the direct lending arena where Blue Owl plays. So there's these cockroaches, there's these cockroach statements, and they kind of go back and forth on this.
Speaker 2:But, the the history of Blue Owl is also interesting. It's this, like, merger between a few different, a few different companies here, and it's part of this, broader, boom in in alternative Blue Owl
Speaker 1:is was the primary lender for, Coraweave.
Speaker 2:Coraweave, and they've also done Stargate. They've done a ton of stuff.
Speaker 1:Yeah.
Speaker 2:And but interestingly, their their their data center business is, I think, like, less than a third of their overall business. They have a lot of other a lot of other stuff going on here. So George Walker, who's the CEO of the old line money management firm Neuberger bur Bergman Berman, he's a cousin of president Bush. He says it's extraordinary what they've done. It was just a start up, and now their $26,600,000,000 market cap compares to a number of large century old financial institutions.
Speaker 2:There were some Blue Owl's backstory entails some rich behind the scenes machinations, but more significantly, it reflects the stunning trajectory of private markets, which have tripled to 26,000,000,000,000 in assets over the past decade. The company's also yes. And and and so, I mean, I do think it's important to keep, like, the scales in mind here. Like, the 1,400,000,000,000.0 seems so big in the venture context, and we think about we think about Sam Altman as a venture backed founder, but he's now playing in a market that
Speaker 1:hyperscaler is game.
Speaker 2:He's yeah. He's playing a hyperscaler game. And so, when I think about it's like
Speaker 1:And so point 4,000,000,000,000,
Speaker 2:that's the same size as the oil and gas market.
Speaker 1:That is same. Manhattan, like, Manhattan project scale data center, the five gigawatt data center that you talked about, them doing this deal with BlueOwl on. Meta's also just did a $30,000,000,000 bond offering Yeah. Which has 4,000,000,000 of 4.2% senior notes due in 2030 and then all the way up to 4 and a half billion of 5.7 senior notes due in 2065. And there was an order book of around a $125,000,000,000 for the $30,000,000,000 issuance.
Speaker 1:So there's a massive amount of
Speaker 2:Huge demand.
Speaker 1:Yeah. So this is not, at least that we know of, what OpenAI has not been raising this style of debt for the business. Yes. And it's unclear if there would be, like, a a a ton of demand for OpenAI's, like, on balance sheet debt
Speaker 2:Totally.
Speaker 1:Given that it's unclear if they're gonna be able to Totally. Spend, you know, what they've already
Speaker 2:Yeah. But but underwriting a data center with OpenAI as a client is very different than underwriting OpenAI directly. Yep. So there's some really funny quotes in this article. Blue Owl is the pretty girl at the dance right now, says Wall Street trader David Williams.
Speaker 2:We're talking many billions in private credit. Ah, yes. Private credit. Though Blue Owl has three lines of business. Private credit, specifically direct lending in private equity deals, is the firm's calling card and growth engine and the straw that's stirring Wall Street Wall Street's punch bowl lately.
Speaker 2:They also have this, like, GP business. If you wanna buy the a GP stake in a alternative asset manager, you can do that through BlueOwl. But what everyone's interested in is this private credit specifically for AI assets, at least from our perspective. I'm sure there's other people that find the other pieces of their business much more interesting. But its core direct lending business has a 145,000,000,000 in AUM out of 284,000,000,000 total, so that's about half the fund.
Speaker 2:And that was conceived, the firm started in 2016 as Owl Rock at the Putnam Restaurant in Greenwich, Connecticut, of course, comfort food at its best. Principals, Doug Ostrover, formerly the o of GSO Capital Partners, now Blackstone Credit, Craig Packer, a former Goldman Sachs partner, and Lipchitz, a former KKR partner. The name came from the wisdom of an owl and the stability of a rock, says Lipchitz. And the website was available.
Speaker 1:That always helps.
Speaker 2:So, instead of relying on in venture, we all think of the GPLP private markets fund structure. Right, Jordy? Alrock changed this. They don't do the typical GPLP split. They use what's called business development companies, BDCs.
Speaker 2:So those those companies issue stock and lend money to businesses, usually those with junk credit ratings. So something like a one off data center that really only has, like, one client and it it's not like Apple. It's not Microsoft. It's not it's not an actually, like, you know, been in business for thirty years. Yeah.
Speaker 2:Not the government. And so it's gonna have a junk rating. It's gonna be higher a higher interest debt instrument. And other and and this has actually been a trend. Other major our alt firms are also turning to BDCs, which support higher yields.
Speaker 2:And so BDCs send some 90% of the interest collected on those loans to shareholders through dividends. So they've basically created, the same structure as real estate investment trust or something close to and so this has allowed them to scale. So two of Blue Owl's BDCs are publicly traded. Others are private. They have Blue Owl Capital Corp, which yields 11.4%, and Blue Owl Technology Finance, which yields 9.9%.
Speaker 2:Both are down about 14% this year, the former from January 1. And so Goldman Sachs recently called the fears overblown about, you know, the risk of falling rates and weakening credit, and they cited Blue Owl as undervalued, noting that it has a stock price to fee related earnings multiple of 21.7, which is 5% below its two year low. So, the stock has been beaten up, but it still has, like, a buy rating from Wall Street firms. Blue Al has generated a stable, highly predictable stream of earnings, says Ostrover, the the other co CEO. It makes no it makes no sense that we're down more than our peers, he says.
Speaker 2:If anything, we should be down less. I love it. I love a defensive Wall Street may be may be particularly wary of direct lending as shares of both Blue Owl and Ares, which specialize in that business, have fallen hard. It's also true that both stocks had previously outpaced their peers. The second leg of the blue owl stool was created years earlier when a Lehman Brothers executive, Michael Rees, started a fund at New Bur Neuberger Berman that bought stakes in asset managers like Dee Shaw.
Speaker 2:This is what I mentioned earlier about buying buying GB stakes. Rees named his endeavor Dial after his children, Dylan and Alexia. He just took his two kids' names and pushed them together, raised its own capital from Coke Industries, and invested in the likes of Silver Lake and Vista Equity Partners. So he was a vehicle to allow you to buy GP stakes in Silver Lake and Vista Equity, which are Got it. Not publicly traded, I believe.
Speaker 2:In 2020, Blue Owl merged with Owl Rock. And so, the the the resulting company was called was named Blue Owl. A bank working on the deal had called it project Blue. So Blue was added to and and Rock was dropped because it was Owl Rock before. And so, the hatching of Blue Owl was problematic to some companies in which Dial had invested, particularly sixth Street Partners and Galoob Capital, both of which sued, claiming the new company created a competitor with sensitive information about their operations because, of course, they own GP stakes in the companies.
Speaker 2:And so in 2021, Blue Owl bought Oak Street Real Estate Capitalist Chicago based firm specialized in sale leasebacks and triple net lease deals as its third business. This wing of Blue Owl has AUM of 71,000,000,000 and is home to the meta infrastructure deal. So they they it's like a remarkably balanced stool. I I I'm I'm I'm sort of shocked when I was expecting it to be like, you know, oh, we hear about Blueell in the meta context, and that's their main business. Or it's a new thing, and it's only 5% of their business.
Speaker 2:In fact, they have they have a pretty, pretty diversified offering across a few different products. And so this wing now has 71,000,000,000 and is home to the Meta infrastructure deal and others, such as Stargate data centers in Texas and New Mexico. Next, BlueOwl is working on AI deals with nScale and Valor Equity to finance purchases from NVIDIA whose chips go for $30,000 and up according to people familiar with the matter. BlueOwl built its direct lending business by borrowing from the Silicon Valley playbook of scale first, monetize later or by underpricing established private credit firms to gain deal flow, and then raising fees later. Most of the firm's direct lending business is done as part of private, private equity buyouts.
Speaker 2:So Tomah Bravo, Blackstone, Warwick Pincus, these companies come in, and then Blue Owl does the debt side of that. These investments generally entail floating junk bonds, a business pioneered by Drexel Burnham in the nineteen eighties, but it was typically done with banks. Now it's done with these private credit firms. The private mic cry private credit market has grown from 2,000,000,000,000 in 2020 to 3,000,000,000,000 at the start of 2025. So, again, I'm like, that's that's growth, but that's not the craziest growth I've ever seen.
Speaker 2:Like, I when I go back to global financial crisis, you know, you hear about these, like, 10x run ups in these derivative markets. Like, I don't know. I I it it just feels like it feels like we're we're still in, like, the early stages of actually ramping this piece of the capital markets and Yep. Marshaling that to the really crazy stuff. It feels like the crazy stuff's coming in two years.
Speaker 2:I don't know.
Speaker 1:Yeah. And that that aligns with Doug Doug from some the analysis point. He was like, that we're still early We are the debt cycle. We're early. But at the same time, market has jitters.
Speaker 1:I was working on our 2026 merch Yes. This morning. Yes. And I had about an hour call. And so I missed the fact that NVIDIA's down 4%, CoreWeave's down 8%.
Speaker 1:And, it's a little bit shaky out there. We're certainly not in white suits today.
Speaker 2:No. No. Well, let's let's go over to the timeline. Let's go over to, some of that other news that, we wanted to touch on today. But first, let me tell you about Cognition, the makers of Devon, the AI software engineer, crushed your backlog with your personal AI engineering team.
Speaker 2:So Alex Heath has a scoop here in sources. During a recent private call, OpenAI's investors asked about external signs that ChatGPT's growth is slowing. CFO, Sarah
Speaker 1:Fryer This is the external signs where I think, like, App Store data. There were some data out of Europe.
Speaker 2:Oh, yes. Yes. Yes.
Speaker 1:That's right.
Speaker 2:That's right.
Speaker 1:And it was hard to read into the European data because Europe, Europeans
Speaker 2:They don't work ever.
Speaker 1:No. I mean, it was coming off of summer. Right? And and, you know, ChatGPT is a popular student.
Speaker 2:But European summer hasn't ended yet. European summer ends late December.
Speaker 1:No. I have to push back on that because when the French television network came That's true. They were clearly done. That was about a month ago. They were clearly back from summer holidays.
Speaker 1:And they wanted to learn about the AI talent wars.
Speaker 2:Yes. That's true. That's true. No. We're obviously joking there.
Speaker 1:Anyway, so there's
Speaker 2:So we've been yeah. There's been early warning signs. Yeah. Walk me through some others.
Speaker 1:So I can walk through Alex's coverage.
Speaker 2:Please.
Speaker 1:It says on Monday, OpenAI's CFO Sarah Fryer held a private she was really hoping to just not be in the news cycle this week. But when you're the CFO of one of the most important companies in the world, that comes with comes with the job.
Speaker 2:What's stockholders leaking this?
Speaker 1:Yeah. That's
Speaker 2:probably You're my an investor, and you're leaking bad news to sources? What are you doing?
Speaker 1:Yeah. What are doing?
Speaker 2:Did you get out or something? Like, are you have you you somehow facilitated some short position? Yeah. Are
Speaker 1:you Jumped off the call. Mark
Speaker 2:This is very founder friendly. Whoever's doing this whoever's doing this is not very founder friendly.
Speaker 1:That's Okay. Anyway, take take me. Anyway, Sarah Fryer held a private quarterly earnings call with the company's biggest investors. As usual, the numbers she shared were mostly up and to the right. But behind the strong top line figures, a quieter question hung over the call.
Speaker 1:Was Chat momentum starting to slow? During the Q and A portion of the call, sources say, Fryer was asked to reconcile ChatGPT's medi meteoric growth in weekly users from two fifty in September 2024 to over 800,000,000 now
Speaker 2:Mhmm.
Speaker 1:With external signs that the app's growth has slowed in recent months. Close followers of OpenAI's business have been whispering about these signals from research firms since late summer, but this was an opportunity for company backers to hear directly from leadership on the matter. After telling the investors to take third party estimates with a grain of salt, Fryer acknowledged a chink in ChatGPT's armor. She said time spent had declined slightly in response to, quote, content restrictions. The company rolled out in early August.
Speaker 1:She then referred to the loosening of those restrictions that CEO Sam Altman has said will be implemented for adults in December. So this is them. Sam came out and said, we're going to allow erotica on the platform. And Sarah says, in OpenAI expects the decline in time spent to reverse. And so this reminded me of conversation we had about exactly a month ago where I said, I don't think them announcing that they're getting into erotica
Speaker 2:Mhmm.
Speaker 1:Is a sign of strength. Don't I don't think that's something that you do you do just because you want to. Right? In my view, it felt like clear I mean, clearly, there's user demand for it. Yeah.
Speaker 1:But at the same time, that felt like something that you that they would do in order to stimulate growth Mhmm. While they get a bunch of other monetization online. Right? So, like, commerce, ads, etcetera.
Speaker 2:Yeah. No. No. No. That makes sense.
Speaker 2:Yeah. I mean, the original, like, founding team at OpenAI was incredibly idealistic. Right? Like, incredibly, like Yeah. You're going to work on a nonprofit on, like, super intelligence, like AGI.
Speaker 2:Like, you you you you truly are working on, like, what you see as one of the most important problems, what I agree with is one of the most important problems. Then, of course, like, you know, eventually, the eventually, the company evolves and you and you bring in business leaders. But at the same time, like like, I I I do believe that when they say, I want to cure cancer. I believe that.
Speaker 1:I I believe that too. And so the reason I reacted strongly to it was that Yes. There had been messaging, you know, around the same time of Yes. I don't wanna be in a world where we have to decide between curing cancer and free education for the world. Yes.
Speaker 1:And so then at that same time deciding we're gonna do erotica
Speaker 2:It was very weird timing. To was very weird it was very weird timing that those two statements, like, came out one after another.
Speaker 1:I'm yeah. I'm actually surprised why they're waiting Yeah. Till December to roll out
Speaker 2:Yep. The adult content. So in this scoop, do we have any, do we have any specific data on on what exactly, what exactly, you know, is indicated in terms of ChatGPT's growth slowing? Can we actually try and define that a little bit more? Is that is that users?
Speaker 2:Because there were already at almost a billion users. Is it is it time on-site? Is it monetization? I mean, deceleration, we were talking about this. Like, OpenAI has decelerated revenue before because they I think they tripled, and then they went to a doubling.
Speaker 2:Or they went or they were quadrupling, and then they went to a to a to a tripling. And so they actually decelerated in 2024, and then they reaccelerated in 2025. And so I was kind of saying, like, well, you know, there's a good chance that you could see deceleration in the future. It's happened before. Like, to be accelerating forever is is basically impossible.
Speaker 2:But it would be interesting to track exactly how how ChatGPT's growth is slowing. There certainly feels like there's just a level of saturation. Do you have the stats?
Speaker 1:Yeah. So meta so SimilarWeb put out some information on month over month change in total visits to leading Gen AI tools. Mhmm. ChatGPT is at the bottom of a list that includes Gemini, DeepSeek, Perplexity, Grok, Claude, Copilot, and Meta AI. The key difference here is that like It's huge.
Speaker 1:ChatGPT is so much bigger
Speaker 2:Yep.
Speaker 1:Than these other platforms that they could still be adding more users Yep. On a on a on a on a per user basis than these other tools, even if their growth is slower.
Speaker 2:Yeah. Yeah. That makes sense. Yeah. Tyler, what do
Speaker 4:I mean, they do expect their growth to slow down. So, like, from this is Epoch AI. They it was like Oh, yeah. OpenAI revenue estimates. Yeah.
Speaker 4:So 2025 is 13,000,000,000, and then they expect 2.3 x, 2026, two x in 2027, and 1.6 x. Yeah. So, I mean, it's not like they're just saying, like, it's it's gonna go from two x to three x to four.
Speaker 2:Yeah. Exactly. So I wonder I wonder how much of this is just framing something that was sort of already priced in as a as, like, a bad thing. Like, I feel like people were expecting deceleration. And so if if she's if she says, like, if she's on the call and she says, as expected, we're really big.
Speaker 2:We're gonna be decelerating the level of new users that we're adding.
Speaker 1:And then I don't think she should say that.
Speaker 2:Yeah. Maybe that would be bad framing. I don't know. It just doesn't seem that crazy.
Speaker 1:Guess she should say that.
Speaker 2:I don't know. It doesn't seem that it doesn't seem like that bad of a thing to say. Like, the like like, Meta is not accelerating top line, top line users. They have, like, 3,000,000,000 users. Like, no one's expecting them to accelerate top line users.
Speaker 2:Maybe, like, randomly, one quarter, they accelerate, but not continually. And so I don't I don't know. It it just feels like an odd thing. Did did you get a chance to read the Ed Zitrin article, this thing?
Speaker 1:I did, but I didn't I felt it, Ed is such a massive OpenAI hater Sure. That I think it was hard to and the sources were pretty unclear. Mhmm. It was hard to read too much
Speaker 2:into it. Okay. Well, let me tell you about figma.com. Think bigger, build faster. Figma helps design and development teams build great products together.
Speaker 2:You can get started for free. We should we head over to some timeline? What else people are saying? Cairo Smith says, it's going to be very funny when LLMs plateau around a 120 IQ. And what we've created is just a digital guy, not a god.
Speaker 4:I mean, doesn't make any sense. If we have, like, infinite digital guys, that's, like, literally a guy is just, like, a worker. Yeah. If we have infinite workers, that's, like, insanely bullish.
Speaker 1:Yes. It is still be bullish,
Speaker 2:but but we've been
Speaker 1:prompt you know, people are promising.
Speaker 2:He didn't say bearish. He said it's gonna he didn't say it's gonna it's gonna collapse the economy when we just get a digital guy. He said it's gonna be funny.
Speaker 5:I
Speaker 4:agree. I guess that's true.
Speaker 2:It's funny.
Speaker 4:But this is still like a very bullish take. I think people you I think you might read this as like being he's kind of bearish.
Speaker 2:Yes. Yes. Yes. Yeah. No.
Speaker 2:I know. I know. I think you're right. If you get a digital guy, that's pretty powerful. Because guys can do a lot of stuff.
Speaker 2:It's valuable.
Speaker 4:I love guys.
Speaker 1:Yeah. You need a guy for everything.
Speaker 2:You do need a guy for everything. And you will in future, you will that opportunity.
Speaker 1:Right? Yeah. That's class has apps. Yes. The wealthy have guys.
Speaker 2:Yes. Yes. Yes. This is could follow-up that. And the apps get better with the the apps get better with AI agency, AI agents.
Speaker 2:Right? Because you you you you have you have an app that acts a little bit more like a guy than than a Yep. Than an app.
Speaker 1:Real quick. Scoot in the ex chat. What's up? Almost bought a counterfeit TBPN hat.
Speaker 2:No way. Watch out.
Speaker 1:Is a counterfeit TBPN store. These are not by us. They've made it look like it it's by us. I'm not gonna name the link. Mhmm.
Speaker 1:But we have not sold any merch. Mhmm. We will make the merch available You're working on as soon as soon as possible. I was working on it this morning before the show. So it's coming, but do not buy any of the counterfeit merch.
Speaker 1:My big concern with that site is I don't even know if they ship it.
Speaker 2:Yeah. That is a big question.
Speaker 1:They and they also made, like, a 100 products.
Speaker 2:They made so many products, and I did email them. And I and to be clear, I
Speaker 1:emailed lawyers emailed them, and we've submitted a bunch of take down.
Speaker 2:Yeah. When I when I emailed them, I was like, hey. Like, I assume, like, you're just a fan. Like, I was I was being too nice. I was being golden retriever mode.
Speaker 2:But I I did say I was like, hey. Like, I, you know, I appreciate this idea. This is this is very cool that you're enjoying the show. But, like, we just don't want people to get confused. We have our own plans the store.
Speaker 4:They're like, okay. I'll make
Speaker 2:$200 of product. They just didn't they just didn't respond at all. And so then we we sent a takedown notice and we will be fighting that tooth and nail.
Speaker 1:So Yeah.
Speaker 2:Stay safe out there.
Speaker 1:But please don't buy it because we have nothing to do with it.
Speaker 2:Tyler, did you get a chance to read Fiji CMO's latest blog post? Moving beyond one size fits all.
Speaker 1:I hope you didn't Yeah.
Speaker 4:Read it. This was Saddie.
Speaker 2:Sat your ass, David.
Speaker 4:We talked about this for a tiny bit yesterday.
Speaker 2:Yes. Yes.
Speaker 4:This was just the the 5.1 release. Yeah. It nothing I would say super substantive Mhmm.
Speaker 2:In
Speaker 4:it. She kind of is talking about how I I think with the with 5.1, they were
Speaker 6:And like
Speaker 1:we made our digital guy faster, better. Yeah. Stronger.
Speaker 2:Is that what
Speaker 4:it the the EQ Okay. Of the model a lot Yeah. Rather than IQ. Yeah. So that's why you see a lot less benchmarks.
Speaker 4:I think it's just hard to actually benchmark that kind of stuff. Yeah. But the actual, like, style of the model, talking about kind of safety ish stuff where there's, like, you know
Speaker 2:I mean, it is it is crazy following this company so closely because in here, there's a line that says, with more than 800,000,000 people using ChatGPT, we're well past the point of one size fits all. An 800,000,000 sounds amazing, except I feel like I heard the 800,000,000 number, like, two months ago, and I feel like they have been accelerating so fast.
Speaker 1:You would expect them to be at
Speaker 2:900. 900. Exactly. And so the fact that they're repeating the 800 number is, like
Speaker 1:They're like, sorry we can't add a third of The United States every month.
Speaker 2:I know. I know. I know. It's very, very high stakes. It's very it's very impressive what they built, to be clear.
Speaker 2:But I I just I am really keyed on that, like, 800, 800 because I was I was excited. They were gonna hit a billion. It was gonna be a big moment. And and yet it it feels like maybe that's a next year to a next year goal. But Nir Syan has been going back and forth on this.
Speaker 2:Nir said, ChatGPT is officially in its Fiji CMO phase. If you're wondering why the upgrade doesn't come with benchmarks, have fun. Rune says you're con you're you are confidently wrong about the internal dynamics of this. It could be better summarized as an infra cleanup. And Nir says, the source for my top tweet is Fiji's blog post from today when which discusses the release and its goals.
Speaker 2:I don't really know what else to say. I don't know. Are I'd like is there hunger for benchmarks anymore? I I I I might actually take the other side of this here, and say that, I like that they're getting away from benchmarks. I don't I wish they didn't do a 5.1.
Speaker 2:I don't want any more confusion. What is 5.1 versus five? Just make it better and don't do a release. And certainly don't tell people because what if people you
Speaker 1:to say because you're not in love with a specific version, John.
Speaker 2:I'm in love with five. I'm in love with five, Rune. Bring back five. I don't like 5.1. I need five specifically.
Speaker 2:Not four o. Not 5.1. I need five.
Speaker 4:Five, please. I will say,
Speaker 1:when Just put the five in the bag, Rune.
Speaker 2:Yeah. Come on. Bring back five. Bring back five. We need to we need to cyberbully Rune until we bring back five.
Speaker 2:Even the most minor tweak to the model is unacceptable.
Speaker 4:Well, you can still use five pro. I know. So that's that's gotta account for something.
Speaker 2:Only five thinking.
Speaker 1:Okay. But but I I
Speaker 4:was I was saying when GPT four,
Speaker 2:like Yeah.
Speaker 4:GPT four, like not four or anything Yeah. When that was the the best model, they would do updates. Yeah. They they wouldn't like say, oh, this is a new model. Yeah.
Speaker 4:And people could definitely tell.
Speaker 2:Oh, sure. Sure. Were
Speaker 4:like, okay. They released a model. It's worse. Mhmm. And then
Speaker 2:One time.
Speaker 4:Twitter would hate it, but then I I think
Speaker 2:So so so you think you think putting a putting a version number actually helps, like, fight back against that? Because people are like, oh, I I get it why it's worse. You you you changed it. Like, instead of, like, there being a surprise under the hood.
Speaker 4:I think it's more it's just, like, easier for people to tell it that it was actually a change when they're noticing something that they've been depending on. Yeah. Yeah. Yeah. It's it's a little different now.
Speaker 2:Like Yeah. I just don't understand why you're why you're surfacing it in the UI of a of like, like, if I open my ChatGPT app at the top now, it says ChatGPT 5.1. Like, this is a consumer iPhone app. It is today's Pulse is here, talking about Blue Owl Stargate investment, and ChatuchPety 5.1. And I just have to wonder if, like, the the 5.1 is, like, unnecessary.
Speaker 2:Like, if I open up Instagram, it does not tell me what version of the Reels algorithm I'm on. They're doing to change it every day. Like, just change the algorithm all the time. Just make it better. And, yeah, if you make it bad, I'm gonna churn.
Speaker 2:So don't do that. Make it better every day forever and just keep shipping. Ship every single day. Like, I'm sure that internally, there are version numbers of Google search. Right?
Speaker 2:Because they push to, like, a main GitHub branch or something or whatever they use for their mono repo. But, like, there is version tracking for, like, the reals algorithm. They just don't surface that to the user. So I don't know why they're surfacing 5.1 to users after there was, like, so much pushback over four zero five five, all this other stuff. It seems like I don't know.
Speaker 2:It seems like a mess. You know what doesn't seem like a mess? Vanta. Automate compliance, manage risk, and accelerate trust with AI. Vanta helps you get compliant fast, and we don't stop there.
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Speaker 1:There was one more note from Please. Alex Heath's article on OpenAI that actually, I think is worth sharing. He says after Meta's last earnings call, sources say and this is confusing because the name of sources is
Speaker 2:I don't wanna hear what Alex Heath has to say specifically. Give me some, like, people who I close to the don't I don't really I don't wanna know what sources says. Wanna know what What sources people close to the matter.
Speaker 1:Sources say that sources say Okay. Sources says that sources say CEO Mark Zuckerberg joined an internal employee q and a and shared a warning about the AI bubble. First, he shared a breakdown of how different players from startups to big tech names like Meta should think about timing their bets. He described three camps in the industry, optimists who see superintelligence emerging within two to three years, moderates who expect breakthroughs by the end of the decade, and pessimists who think it'll take well into the 2030s. Each outlook, he said, dictates how aggressively a company invests.
Speaker 1:Then he expounded on a version of the answer he gave me recently in our last interview. He noted that while unprofitable startups like OpenAI and Anthropic risk bankruptcy if they misjudge the timing of their investment. Meta has the advantage of strong cash flow. Mhmm. He also made the point that while big tech has historically been relatively debt debt free Mhmm.
Speaker 1:Compared to large companies in other sectors, the AI infrastructure race is leading Meta and its peers to start using leverage in a more normal way relative to their size.
Speaker 2:Mhmm.
Speaker 1:Like he told me in September, Zuckerberg acknowledged to employees that Meta's market cap could suffer if his timing is wrong and the bubble bursts, But the message was clear. We'll have the balance sheet to survive and emerge stronger than most on the other side. So anyways, Super Daria was quoting that and said, the obvious end game in the next two to three years is that Microsoft acquires OpenAI, Google acquires Anthropic, and Tesla acquires XAI. Only the large caps survive.
Speaker 2:That's a nuclear hot take. Is crazy, crazy. How would that even I don't know. Could Microsoft get the rest of OpenAI? I mean, I guess they probably have
Speaker 1:Depends on the price.
Speaker 2:Size. It's a $4,000,000,000,000 company versus a 500,000,000,000. Yeah. I don't know. It doesn't seem like impossible.
Speaker 2:Let me tell you about graphite.dev code review for the age of AI. Graphite helps teams and GitHub ship higher quality software faster. It's very free. I wanna run through some more of these posts. Yuchin Jin says, in contrast, OpenAI employees stayed for two plus years, sold $6,600,000,000 of equity last month.
Speaker 2:Many hit the $20,000,000 cap. Morale and vibes are high, but so is the turnover rate. New OpenAI hires are often shocked by how many Slack accounts get deactivated each day. This is a a screenshot of an interaction between Jack Morris and Lang Chen Liang Chen Luo at XAI. Jack says, there are dozens or perhaps a couple 100 x OpenAI, xAI, Google, DeepMind researchers founding companies in the current climate.
Speaker 1:And like I said,
Speaker 2:the simple answer, the liquidity of anthropic options is the worst among those frontier lab.
Speaker 1:Is talking about how a lot of people have been leaving various labs
Speaker 2:Mhmm.
Speaker 1:Less people have been leaving anthropic. And so Liang Chen is saying the simple answer.
Speaker 2:Yeah. And so Andre Karpathi says, bull's eye, it's interesting how large of a fraction of people don't see the dominant first order term that drives behavior of people in companies. You you can construct a powerful world model just by understanding one, just by one understanding the system and two assuming this, there is only this single term, like liquidity, how much, how much cash you have. What'd be interesting is if you could, is is if companies started offering, liquidity in the form of annuities. So imagine you have an employee who's like a rock star.
Speaker 2:They're gonna sell $20,000,000 of stock, and they're gonna basically be post economic. If you could instead say we're we're we're we're going to be paying you out like, you're selling now, but you're but we're we're when you get a billion dollars a year or something, there needs to be some way to, like, sort of cap. Is there is there some way to cap, the actual amount? I guess 20,000,000 was the cap. But, I I I don't know.
Speaker 2:There there there there's some way to to to deal with this. Like, you know, if you don't get an employ if you don't get employee liquidity, like, they'll leave for something else. Just go somewhere else that pays them a higher salary. If you give them too much liquidity, they'll leave and start new companies. Very, very tricky to manage the manage the team.
Speaker 2:But that is the nature of these these companies.
Speaker 1:Chad Byers. That is his real name. He is a Chad in the literal sense.
Speaker 4:And figurative sense.
Speaker 1:And figurative sense. He says, one of my strongest beliefs is that it's gonna take twenty plus years to get penetrated into the real economy. I filled out a piece of paper at the doctor's office last week.
Speaker 2:I filled out a piece of paper at the doctor's office last week too. It was crazy, and I was wondering, like, when will we see a fast takeoff in DocuSign? Fun. I I finally realized why DocuSign has so many employees because you need to go to every doctor's office in person, apparently, for decades to get them to use online form filling technology. Like, general SaaS really does not has not has not permeated as much of the economy as people think.
Speaker 2:A lot of people still on spreadsheets for all sorts of stuff. A lot of people still on paper and pencil. There is a you you know, we joke about being pro ramp, anti paper receipts, of course. There's a company that makes paper receipts that's worth $20,000,000,000. $20,000,000,000.
Speaker 2:There are fax machine companies. The fax machine industry is still over a billion dollars. Still a billion dollar industry. Crazy.
Speaker 1:I would think it would I would think it was actually more.
Speaker 2:Yeah. Maybe that's maybe that's too small. It's it's sort of hard to, like, calculate because a lot of these things have been, like, rolled up into other companies. And
Speaker 1:Big fax. Big fax wants
Speaker 2:you Like, think They want you to think it's small. One of them. And and so, like, I don't even know if Canon breaks out their fax business anymore because they sell so many, cameras and other equipment. But, yeah, it's fine. It's interesting.
Speaker 2:Mir says IMO, in my opinion, the entire AI field switched from explore to exploit two years early. Everyone convinced themselves, no. This isn't the case. Look at our exploration. And it's like watching someone go on a 50 foot walk and find a cool tree when the entire continent is still covered in fog of war.
Speaker 2:Now that the terrain seems known, it should be harder to convince yourself. I suppose this makes sense given a lot of people hint at being good as gone as soon as they have enough money. But no. Not me. I've been gone for ages already.
Speaker 2:That's a very funny post. I suppose weren't we talking about this yesterday, this idea of, like, of, like, where will the next innovation come from? Where will the next breakthrough come from? Will it come from, any of the any of the the the like, will it come from x AI?
Speaker 1:Will it come from DeepMind?
Speaker 2:Yeah. Like, how much do you need the college campus? How much do you need that environment?
Speaker 1:Yeah. Will it come from a university?
Speaker 2:A university. The universities seem to have not like, it's very odd that the university system did not produce the transformer paper. Feels like the perfect thing to come out of a university setting.
Speaker 1:Yeah. Mean, it's really tough right now. You can stay in a university system and be a student and be taking on debt. Or you can go work at a lab Mhmm. And make have a good shot.
Speaker 1:At least if you did this a few years ago, have a good shot of making $20,000,000 in a few years.
Speaker 2:And Yeah.
Speaker 1:It's hard to give up that kind of opportunity.
Speaker 2:Yeah. This Wall Street Journal article has given more context on the AI boom. It says the AI boom is looking more and more fragile. AI stocks have swung downward as doubt rises about sustainability and payoff. Perfect isn't good enough, and any sign of weakness is a disaster.
Speaker 2:This is what's happening. It's like you double revenue and your stock trades down. It's very, very odd, but everything with price Core Revue affection.
Speaker 1:Core Revue, again Yes. Is the only Neo Cloud in the platinum tier Yes. Semi analysis Yes. Is down 45%.
Speaker 2:That is remarkable. Month. It's like they they built a like, by all accounts, fantastic product. Fantastic product. I mean, like, I don't know.
Speaker 2:Maybe Sammy has got it wrong, but I don't think so. But it feels like they built something that as infrastructure delivers at the level of the hyperscalers, just like a fantastic product. And yet, the the market, like, sort of ran away with that narrative, and now it's pulling back a little bit. So, recent history suggests that the gloom won't last, but the shakeup serves as a strong reminder that the early years of AI pose a challenge for investors accustomed to measuring returns on a twelve month time horizon. Generative AI services require massive data centers and state of the art chips and server racks that don't come together quickly.
Speaker 2:The companies at the heart of the of AI are now talking about years, plural, of all major investments still ahead. So everything has kind of sold off a little bit. Oracle, is down the most from its three month high. NVIDIA's down a little bit. Google is neck and neck.
Speaker 2:They're doing great. And oddly, Apple didn't even make the chart because they're not they're so not indexed to AI right now. Last the latest episode of of fragility started last week when shares of some of the sector's leading lights lost ground after a broad based recovery on Monday on news of a possible end of the government shutdown. AI stocks fell again Tuesday. NVIDIA's down 4%.
Speaker 2:Today lost 7% last week, slipped another 3% on Tuesday, while leaving it well shy of its $5,000,000,000,000 market cap.
Speaker 1:Yeah. Looking at the trailing twelve months, Apple is up 21, and Microsoft, which owns a third of OpenAI, is a huge AI beneficiary, has invested a ton in it, is only up 18%. Wow. So Apple, which has been going through
Speaker 2:This was the this was the secret. Just don't invest in AI.
Speaker 1:Do nothing.
Speaker 2:Do When? Just don't do it. Just skip it entirely. No. Just do nothing.
Speaker 1:Tim Cook's like, wait. Why would I spend a 100,000,000,000 on CapEx? I can
Speaker 2:And to be clear, I can
Speaker 1:he's like, I can sign up for Chad So hard. For, like, $20.
Speaker 2:So hard. She's like, yeah. You know, we have this we have Safari. We have we have this web browser. You can go to use AI from there.
Speaker 2:Just do that on your phone. I don't care. That would have been the correct thing instead of getting over their skis a little bit on the branding side. Fortunately, not on the financial side, so they've done very well. There is, of course, real reasons to worry about the sustainability of the boom.
Speaker 2:Chief among them is that there's far more AI computing infrastructure spending than there is AI revenue, a gulf widening by the day. OpenAI says planning to spend $1,400,000,000,000 in the next eight years, but is only pill pulling in around 20,000,000,000 of annual revenue today. And it lacks a clear business model to reach the hundreds of billions it needs within the next few years to keep the spending growth going. OpenAI is projecting losses will swell to 74,000,000,000 in 2028. So skittish has the mood become that CEO Sam Altman felt the need last week to defend the company on x, saying the spending was understandably causing concern.
Speaker 2:Wow. He says he understands your concerns, Jordy. He pointed to his plans to boost revenue with new consumer devices, robotics efforts, and AI cloud computing service, none
Speaker 1:of And which currently this is why when we were Monday after that interview when we were talking about it, saying that wasn't a strong answer because all those things seem like businesses that will lose a lot of money even if they're successful Yeah. Until they can reach some huge scale. Look at Meta's efforts in in hardware. Look at early days of any hyperscaler. Look at any robotics company.
Speaker 1:Right? These are not cash engines. They're cash incineration engines that could one day
Speaker 2:If they want if they wanna get more, like, cash generation, stop incinerating so much cash, OpenAI should should yeah. I know they're doing a lot, but they should expand into, like, just just rolling up HVACs HVAC businesses. Just buy a bunch of HVAC businesses.
Speaker 1:Into the workflows.
Speaker 2:Don't even in. Agents. We don't even need to do that. Just buy a good, durable business and roll it up.
Speaker 4:Plumbing, electrical Roofing. Roofing.
Speaker 2:Storage units. Store there's there's a lot of good money in storage units. If they can some storage units. This It's Just buying storage facility and then just say,
Speaker 1:click in Cloud
Speaker 2:in percent off.
Speaker 1:Storage storage storage units that look like clouds.
Speaker 2:Yeah. Yeah. Cloud storage. Lawn mowing businesses. They could get into some a bunch of lawn gardening businesses.
Speaker 2:There's a whole bunch of opportunity
Speaker 1:there. Yeah.
Speaker 2:I mean, just buying multifamily homes. Just buying some multifamily homes.
Speaker 4:Single family homes.
Speaker 2:Single family homes, getting the rent payments. They put the money in. They buy the house, and then they get the rent payment, and that's how they make the money. And I I think that could be it's a proven business model. Like, we know it works.
Speaker 2:It works for a lot of people. A lot of people, they they start with one single family home. They grow it. They keep Box
Speaker 1:Box of Oranges. OpenAI Property Management LLC. So they have the property management company. And
Speaker 2:they actually That's good.
Speaker 1:Yeah. They own the the the the the properties with another. Yeah. And and so they can they can actually play both sides. Yeah.
Speaker 1:Play both games.
Speaker 2:You know? They could get into drop shipping. Woah. Think about it.
Speaker 1:They could set up a TPP and merch store.
Speaker 2:They could set up merch stores.
Speaker 1:Counterfeit merch? For
Speaker 2:sure. For sure. That's what you know, they keep talking about agentic commerce in the in the ChatGPT app. Imagine, like, you go there, you're just like, need some I need some I need some shirts. And it just it it just instantly sends you some T shirts.
Speaker 2:They're gonna need to drop ship it. They could also launch a course. You can launch a course.
Speaker 1:How to build an AI startup?
Speaker 2:How to build an AI startup? $2,000. Buckle up, buddy. Get ready to pay $2. Sam Altman's already been driving around in hypercars.
Speaker 2:You know? Like
Speaker 1:He has the garage.
Speaker 2:He has the garage for it.
Speaker 1:To be a course,
Speaker 2:bro. To be a course, bro. That'd be great.
Speaker 1:Actually, he has a much better collection.
Speaker 2:He really does. I would pay for his course, honestly. I would 100%
Speaker 1:You see a guy in a in a in in a p one, and he's telling you Yep. I will teach you to be rich. I will teach you to build an AI.
Speaker 2:I mean buying back course of years. How to how to do deals from Sam Altman? I would 100% pay $2 for that course. I'm not kidding at all. Like, 100%, it's worth it.
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Speaker 1:Sandisk is only down 15% today. So Sandisk. By the way, drugs. Yeah. The the White House last night tweeted, we are so back in all caps.
Speaker 2:What was that? What did
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Speaker 1:In the chat says the OpenAI McLaren Museum.
Speaker 4:Open up a
Speaker 2:The museum. The McLaren Museum. Just tickets. It's just like adding to our revenue. Like, we're gonna charge $25.
Speaker 2:You can bring your kids. Like, you
Speaker 1:I would visit. I would buy the course. I would go to the museum.
Speaker 2:Yes. Well, our first guest of the show is in the Restream waiting room. Let's bring him into the TV at Ultradome. How you doing, Spencer? To What's see up, guys?
Speaker 2:What's up?
Speaker 1:I hope you haven't been watching the last five minutes because we're having
Speaker 7:We've been all over place. Fun. We had all over place.
Speaker 5:You to know that I I have been watching. And, guys, we are so back from the White House. That's gotta be a reference to the government shutdown. Oh.
Speaker 2:Oh. Yeah. Okay. Okay. I it was forgot about the in the markets.
Speaker 2:I forgot about the government. Think about the shutdown. Right. Okay. Well, that's fantastic.
Speaker 2:For those who don't know, you introduced yourself a little bit. You give us a little backstory.
Speaker 5:Yeah, everybody. I'm a general partner of Cotu, help lead our growth fund, focus on late stage transcendent technology companies. So we work with companies across, of course, software, AI obviously, and then also some stuff in hard tech, SpaceX and real companies like this. So we're a twenty five year old hedge fund, launched a private business fifteen years ago. We're about 70,000,000,000 of AUM today across
Speaker 2:Overnight success.
Speaker 5:There you go.
Speaker 1:Mallet. We're we're looking for the mallet. There it is. It's in front of your computer, John. There we go.
Speaker 1:This is for Kotu. We are thrilled to have you. I've had many fun conversations this year. First one on the air. The reason for today's appearance is none other than Cursor.
Speaker 2:Oh, yeah. What's new in Cursor world?
Speaker 5:Right. The the the GPT rappers are Yes. You know, their demise was maybe greatly overexaggerated on x Yes.
Speaker 2:Over the
Speaker 5:past few years. Look. We we've we've gotten to know Cursor for a long time. We just think it's a really special company. Mhmm.
Speaker 5:Michael and his team are incredible. I'm sure maybe you guys saw Breeze piece about the company, the unique culture
Speaker 2:Yeah.
Speaker 5:How they approach hiring. Yeah. I've just never seen a company with such a deep bench of really talented young folks. And then look on the growth, the growth speaks for itself. Right?
Speaker 5:I mean, we're seeing folks on Twitter today talking about the company being the fastest ever to a billion of revenue. Right? It's definitely in that air, which they disclosed today being at a billion of revenue. And then if you think about the things that have transpired over the past year with Anthropic coming out with an excellent product in Claude Code, OpenAI responding with Codex over the summer, again, an excellent product. All these things happening at once and then you sit here and you wake up today and say, Cursor's got an incredible business, incredible product velocity, an incredible team.
Speaker 5:And now their own model, which they shared yesterday, is their number two most used model on the platform in Composer. So, you know, there's a lot of good companies in San Francisco. It's kind of a nice thing. Like, in early twenty two, everyone was very worried that all of our '21 investments and everything was just gonna, you know, kind of wipe out. There's actually a lot of good companies in San Francisco.
Speaker 5:It's a great thing. There's there's a decent Narrative violation. Right? Right? There's a lot of good companies.
Speaker 5:There's a few there's a few great companies also, but there are very few of these companies that, you know, the the word of the day or the word I try to always come back to is transcendent. And you kinda know it when you see it or you know it when you're around it. And that's what we think, Michael and the team are doing at Cursor.
Speaker 1:Funny funny story. I Cursor, is I I'm incredibly inspired by by their growth and and user love and and everything they've accomplished over the last few years. But I had a painful moment when they when they when they first kind of exploded onto the scene because I realized that Aman, one of the co founders had been following me since like 2020 or something. I was like, I I I I had this I've I've had this like, you know, lingering fear over the last few years of like, you know, getting followed by somebody, not reaching out, not investing. And, certainly certainly would have been smart a few years ago to, to
Speaker 5:Well, listen, Jordy.
Speaker 7:It's not
Speaker 5:too late. There's still we think there's still a lot of upside, you know? There
Speaker 2:you go.
Speaker 5:So we'll we'll see. Fingers crossed.
Speaker 2:One of the frameworks that I've heard kind of bandied about around what's going on like, a lot of people maybe want this to be more of a winner take all market than it is. And yet when and and yet maybe because of Jevan's paradox or just the nature of like, we it's very hard to understand how much code does humanity actually wanna write. Like, it's potentially a ton. And so you're in this, like, entirely new market that's a blue ocean, and it's massive. And it's just growing, growing, growing.
Speaker 2:And so there's opportunity, and I think you're in a unique position where you you you're you're in a number of names that do overlap somewhat, and there's a lot of opportunity. I'm wondering if you think that's the right framework or or or is there more of a, like, a winner take all monopoly thesis with with this particular market? Or if there's other, historical anecdotes that you go to to kind of understand how this might play out.
Speaker 5:Yeah. I'm I'm I'm not sure what the best I mean, I'll get I'll get to the anecdotes. It's it's hard to find the best analog, and analogs are always difficult, especially for this. We're we're investors in OpenAI, Anthropic, and Cursor.
Speaker 2:Yeah.
Speaker 5:We're also investors in Glean, Harvey, Open Evidence, a number of companies across the kind of AI stack. Our view, right, if you just take a step back, right, people talk about the the bottoms up TAM sizing on developers to your point. People wanna write a lot of code. Mhmm. 30,000,000 developers in the world.
Speaker 5:Right? A fraction of a percent of the global population today. And so what what's that number look like in ten years, twenty years? What should that number be? I mean, that's not really an answerable question, but probably much higher.
Speaker 5:And then if you take a step back tops down, you've got 5,000,000,000,000 of global IT spend, and about a third of that is labor today. So, hey, OpenAI is putting up historic growth numbers, Anthropix putting up historic growth numbers. Right? All that's
Speaker 2:Yep.
Speaker 5:Well, kind of directionally known. We see all these companies growing really, really healthy
Speaker 2:Yeah.
Speaker 5:And doing it in a in a way that we think is pretty sustainable.
Speaker 2:Yeah. Did you have a reaction to, Satya Nadella on Dwarrakesh x Dylan Patel? There there's this interview, and he kind of talks about, it feels like he was kinda sending the signal. I'm gonna steamroll you if you try and build an Excel agent. I'm I'm defending the castle that is Excel, but also on a platform for a bunch
Speaker 8:of other things.
Speaker 1:Is Excel Copilot?
Speaker 2:Yeah. Exactly. And I think that I I think that I I would bet on him being able to defend the castle that is Excel, but I've but I was going back and forth with with, with Jordy about, it feels a lot harder for Microsoft to put up a fight in AI for lawyers because they just don't have a practice there. And, yes, lawyers use Word documents, but, it is a it it it just feels like a slightly different, go to market, slightly different product. And so I'm wondering on how you think about the edges of, like, where the where the hyperscalers or the neo hyperscalers, the Foundation Labs might steamroll versus not, because Cursor is an interesting example of a company that feels like almost steamrollable and yet hasn't been.
Speaker 5:Right. Hasn't been. Right? Yeah. And, again, if you look back at the past, if I told you, hey.
Speaker 5:Claude Code is gonna be this amazing product that's this good and you can use it, you think, oh my god. Right? Curves are good. Like, I'm short in cursor. Totally.
Speaker 5:That's what a lot of people so, again, I just think it's it's you know, we get to sit in a unique spot where we see all these things happening
Speaker 2:Yeah.
Speaker 5:And go, wow. We're just so early in terms of the pie growing Yeah. The the tide rising with all boats. I think I messed up that somehow. To get back to your question around the the hyperscalers and who wins where, It's it's it's hard to say exactly.
Speaker 5:I think that's kind of the big question right now. If you look back at like the beginning of the year, think there was an open question around are are these API businesses gonna work? Is there long term margin in the API business? Right? Is something like cursor just gonna get run over?
Speaker 5:Is something even like Anthropic in the API layer gonna get run over and squeezed? Mhmm. I think now people are kinda going, well, you know, these businesses look really good. They've scaled really nicely despite all these things to your point happening at
Speaker 2:once. Maybe
Speaker 5:now the question is, what's the the architecture of that market look like with the hyperscalers over the next ten years? It's it's hard to know. What what I would say is if you take a step back and you look at the cloud AI revenues for the for the three hyperscalers, I mean, they've been inordinate beneficiaries of this trend. Right? Open, Anthropic Cursor, like Harvey, all these companies are great.
Speaker 5:The hyperscalers have done really, really well. And the move to cloud and the move from on prem to cloud that's being pulled forward by all this is really, really compelling for them. So like, they don't need to worry about winning in our view, right, like lawyers in beating Harvey. Like, that's that's almost it's just further down the economic ladder in my mind for them, but, you know, we'll see. The and then the other point oh, you asked about Excel.
Speaker 5:Yeah. Excel is fascinating. Right? The ultimate, like, dominant software product over many decades. We've been looking for kind of the cursor for Excel Yes.
Speaker 5:For a while. We've tested a few products. We've got an internal team here that builds a bunch of things that we
Speaker 1:built your fair share of models over the years. I would hope. Yeah.
Speaker 2:We we we do
Speaker 5:a lot with with data science and and now increasingly with AI. We haven't quite found something that works yet, but there are a few folks building some interesting companies that were not invested in them yet, so I'm not gonna say what they are. But it's an interesting opportunity. Could, I I think that more likely than not, Microsoft still ends up owning a lot of that.
Speaker 2:I mean, the closed source thing is very important. Like, there is no Versus code in that category. People talk about this with cursor for the for biotech. Like, what's the lab notebook? And I was pushing on, like I was trying to go I I was trying to go deep with an investor who, kind of came at me with a thesis of of cursor for bio, and I was saying, okay.
Speaker 2:Well, if if you're gonna invest in the cursor of bio, what's the Versus code of bio? What's the open source what's the open source standard that you can fork that every bio lab uses? And they're like, oh, well, actually, like, the labs use closed source stuff, and those enterprises do not just wanna give up the data, so you're gonna be fighting for the walled garden and all this other stuff. So, it it it does feel like the, like, the cursor for x model I mean, it was really hot at y c, what, two batches ago. Everyone was doing cursor for x, And it feels like, it was wishful thinking in some markets.
Speaker 5:Yeah. I I think that's exactly right. And what's interesting is for biotech in in particular. Right? And we've heard Sam make more public comments recently right about their effort with the sciences how much time they're spending there.
Speaker 2:Yeah.
Speaker 5:I just think that's where the value ends up accruing is something like an open. Anthropic probably throws their hat in the ring at some point. There's, isomorphic labs coming out of Google. There's the Chai Discovery business, which is a compelling company. There's a number of folks who are kind of building these pseudo lab companies that work with with biotech.
Speaker 5:I think that's probably what ends up being more interesting because to your point, there's not a there's not like a Versus code for biotech today. And there are just very few markets where there even is a Versus code analog. Right? Part of the magic of Cursor is they identified an incredible market where the models worked really well early, which was coding. Right?
Speaker 5:This has been the frontier of model capability
Speaker 2:Mhmm.
Speaker 5:For a while. You had the ubiquity of Versus Code and of the terminal and all these things. Mhmm. And then you just built a really nice product around it and have continued to just push the frontier of what's possible. And now you layer in their own model with Composer.
Speaker 5:So Yeah. We're excited about it. But it's hard to find markets with the same anatomy and structure and kind of opportunity to your point. People are kind of squinting and like kind of forcing ideas a little bit.
Speaker 2:Totally. Totally. Totally. Yeah. Jordy?
Speaker 1:I'm sure I'm sure you guys just try to back visionary founders that don't come to you for help unless they really need it, hopefully. But how do you think private companies how much attention should private companies in AI be be paying to the public markets right now? Because we're seeing so much growth, revenue growth in the private markets. And at the same time, you know, we were just, you know, core core weaves down 45% in the past month. There's a lot of jitters in in the market right now, and it's hard not to pay attention to some of these signals even if even if a company's own revenue growth is is skyrocketing.
Speaker 1:So I'm curious how how you think the best companies are gonna kinda manage through the next twelve months.
Speaker 5:Yeah. I mean, look. The the fortunate thing for the the best companies is almost all of them have a war chest Mhmm. In terms of capital on the balance sheet today. And I think the capital demands of these businesses is a bit exaggerated relative to what what what it is would be maybe my personal view relative to like what's on x or what's in the media around these AI companies.
Speaker 2:Yeah.
Speaker 5:In terms of like paying attention to public markets, right, I think it's I saw Subbu tweeted, I think a few days ago, something around, right, like the the market's been above its fifty day moving average for like over a hundred straight days, like something like a hundred and thirty straight days or something like this. So you know that the market's been good for a while. When you see a stat like that, I think founders are aware of that and are pretty aware that it's a good time to raise capital and fortify a war chest, right, which we've had a really busy q two and q three and now early q four. Mhmm. I think that's kind of the way to think about public markets is, hey, when's a good time to raise capital when my when my cost of equity is lower?
Speaker 5:Yeah. But outside of that, like, I just think tunnel vision is everything. And again, just to to bring it back to Kershaw, like, is a very focused company, very heads down company, doesn't make a lot of appearances. Right? Like, just stays in their lane and focuses.
Speaker 5:And I think that that's kind of the ultimate value. Right? You don't wanna be spread too thin. And look, there's a lot of folks like myself running around San Francisco now looking to give founders money and tell them when the when the value of their equity is is pretty good. Right?
Speaker 5:So we we help give them that signal.
Speaker 2:Yeah. There's also this interesting I it it's hard to measure corrections in the venture ecosystem because they're much less quantitative than just looking at, oh, okay. You could, you know, map the
Speaker 1:Yeah. Correction often looks like, wow. That company hasn't raised in three years.
Speaker 2:Yeah. Yeah. That or, you know, there's some hiccup in in LP fundraising or something. But, there there's there's many corrections in the public markets that I can think of that were, like, hiccups in the private markets. But for the most part, it was everyone just kind of being like, oh, there's some crazy stuff going on.
Speaker 2:Okay. Like, let's cancel a couple meetings and okay. We're back on two two months later. Like, the COVID nineteen, like, sell off was, like, a massive correction in the public markets. And for most for most startups, it was like, yeah.
Speaker 2:You needed to understand what could your company continue, or were you in, like, hospitality or something that was gonna be heavily affected. But for a lot of companies, it was just like, oh, your your your March raise turned into a June raise over Zoom, and it was fine. Yeah.
Speaker 5:I mean, I I I think that's spot on. Right? Like, there is a level where, right. Because we do have a public hedge fund. And so what's interesting is when you have conversations like the one you guys were having prior to this.
Speaker 5:Right? You know, as a hedge fund, you're always behaving in the market. Right? There's always a choice to be making. Right?
Speaker 5:It's very unusual to be in a 100% cash or something like this.
Speaker 2:Yeah.
Speaker 5:Right? Is is is a private fund, is a growth fund, we can just choose to not invest for a while. Yep. Right? We can just wait a
Speaker 2:bit.
Speaker 5:Yep. And for us, the the convenient thing is, you know, we only make I think this year we've maybe added about half a dozen Mhmm. New logos to the portfolio. So for us, we're only making three to five core big investments a year on the growth side right now is kind of our cadence. So it helps us kind of work through whatever that cycle is and just focus on the asset, focus on the entrepreneur, focus on what we view as the ten year trend and story for why this company is gonna be so durable and so powerful over time.
Speaker 1:Yeah.
Speaker 5:But, yeah, like, you don't see it. Right? I think the other thing that's maybe, like, less discussed is we've had a bunch of companies, and I've seen companies outside our portfolio too that were kinda your 2017 era SaaS businesses that have really seen a nice bounce back and a nice acceleration over the past few years with AI. Really, like, reinvention is the wrong term. It's just kind of like some product extension
Speaker 2:Yep.
Speaker 1:And pull Yeah. The CEOs and the management teams and the the teams are just reenergized and excited because there's this new capability that you can integrate across your entire platform.
Speaker 5:Totally. And if you if you were some if you were some, you know, flavor of a source of record, your ability to now use that data Totally. And create economic value around it is just inordinately better. So yeah. I mean, look, the the market's been up for a while.
Speaker 5:It's definitely interesting.
Speaker 2:Yeah.
Speaker 5:But for us on the private side, it's a little bit easier because we just get to focus on the companies.
Speaker 2:Yeah. How do you think people are do you think people are reading too much into Sarah Fryer's, like, private phone calls these days? Or, I mean, it it feels like, oh, you know, OpenAI has, a billion users. Deceleration might be sort of, like, expected. Like, you know, people don't excel they don't expect Meta to accelerate on on DAUs.
Speaker 2:You have any thoughts on, like, what's going on there?
Speaker 5:I've got a big term because I I knew this would come up, and so I thought of this before. This was my one big term here.
Speaker 2:Okay.
Speaker 5:Anxiety displacement, guys. We've got anxiety displacement. That's what's going on. You know? Like, people talk about anger displacement.
Speaker 5:Right? All these things.
Speaker 2:Yeah.
Speaker 5:But market's been up for a while. Like, that's a that's a that's like a a fact. Yep. Oh, but ChatGPT is less than three years old.
Speaker 2:Sure.
Speaker 5:It's a product. Right?
Speaker 2:Yeah.
Speaker 5:It's growing really quickly. Right? It's it's they've disclosed they're almost 10% of the world is using the product weekly. Yeah. Right?
Speaker 5:Like, that's pretty remarkable. Right? The market's been up a while. We had, like I still think as a society, people haven't really processed the whole lockdown thing. We had the government shut down for, you know, a month and a half.
Speaker 5:Like, there's all this stuff going on, and there's all this anxiety. And I do I think, like, that's been very displaced because people you know? Like, OpenAI is a great company. It's a remarkable business. There's just it's hard to think of an analog for what they've achieved in less than three years is a is a product.
Speaker 5:And I think Sarah and Sam and the entire team do an incredible job. So people are definitely reading too much into anything and everything. Sometimes I think there's a little bit of a malicious spin Sure. On some of the stuff they've put out also just to be very candid about that. Yeah.
Speaker 5:But, yeah, I mean, it's understandable for there to be a lot of anxiety in society in the market, and it's understandable that it gets displaced onto the company a little bit, but, I don't think that's fair.
Speaker 2:Yeah. It was a little weird. I mean, we were reacting to this idea that there's a private call with investors. Like, what type of investor is then leaking, like, potentially bad news to the press? It seemed like an odd chain of events, but there's certainly unlimited demand for bearish takes about ChatGPT right now because, they've they've been on such a tear.
Speaker 4:Every day people log on
Speaker 1:to ChatGPT and ask for what's the most bearish thing about ChatGPT?
Speaker 2:Yep. Yeah. Yeah. Right. Right.
Speaker 5:That's, like, that's the that's the tweet prompt. Like, that's your x maximalizing, like, prompt right there. It's probably something like that. Right? What what chart can I put together or what can I do?
Speaker 5:But look, I like, we'll see. Right? Yeah. Worked with the company for a long time. Think it's an amazing company.
Speaker 5:I've been really impressed by what they've done. I continue to use the product every day Mhmm. A ton. I think 5.1, the new model, is really a nice upgrade too. I enjoy it.
Speaker 5:So, you know, yeah. I think it's I think they do a great job. And I think we can definitely over over rotate on it. But I think We're
Speaker 1:we're excited for 5.67, which would be only a few upgrade cycles away.
Speaker 2:Rolling my eyes at that one. Can't do it. Can't do the stupid six seven memes.
Speaker 1:It's over.
Speaker 2:I'm over them.
Speaker 1:Kailed it just now.
Speaker 2:They're over them. I like that. I I like the new coinage though. The anxiety displacement. It's good.
Speaker 1:Let's talk about other growth funds because that's just a fun fun topic for Yeah. For every every investors to talk about everyone else. Do you think anybody has have you seen any other any other funds kind of blink yet or or or hesitate or or kind of slow down at all? Get a little bit worried or because because it's just like we're seeing every every day we get like half a billion dollars of funding announced on on the show, but that has that's a lag, you know. It has like a these rounds like really got done in in Months.
Speaker 1:Late summer, early fall, and they're just kinda getting getting out there now.
Speaker 5:Yeah. I mean, I'm sure there are some funds that have slowed down. I'm sure there are funds that are deploying more than us and less than us and those things. Right? I'd say generally, the the I think the vibe when you're catching up with someone over coffee or lunch is a lot more positive than over Twitter.
Speaker 5:Right? And there's a lot more of like, well, how exposed am I to the AI trend? Right? Like there are few growth funds that feel like, you know, I've disclosed right, hey, I'm a little behind this and I'm trying to catch up here and there. So Mhmm.
Speaker 5:If anything like I've felt more anxiety about that then, oh my gosh, you know, we just put more money and pick your big premier company and, you know, I haven't slept well about it. So that's not necessarily a a great signal either. Right? That's kinda like an an average signal, but I'm sure there are some folks slowing down. Not sure who they are per se because we have a lot of funds that are definitely deploying a lot more than us in terms of absolute dollars and just absolute number of deals.
Speaker 5:So, for us, we just again, we really just try to focus on our our core, you know, mission, which is, hey, what are gonna be companies that matter as public enterprises, you know
Speaker 2:Mhmm.
Speaker 5:Ten, fifteen, twenty years from now? And in some cases, a little bit sooner. Right? And
Speaker 2:Yeah. When you think about the capital intensive pre revenue AI companies, we had Fei Fei Li from World Labs on. I don't know if they're fully pre revenue, but it feels like a lot of the world model projects, the generative three d worlds, Gaussian splats, the what Google D MIND's working on with Gemma, or is it Genie? Genie is the model. Right.
Speaker 2:It feels like something that I just think will be, like, the next roadblocks vaguely, but there's nothing that you could do to underwrite this against revenue growth. And so and yet and yet a lot of these projects are, like, to do the next thing, we need $200,000,000. How would you as a growth investor even square that, or would you just say, hey. Let's just come back when we can actually see some adoption data?
Speaker 5:Yeah. Normally, we would fall into the second bucket, like, generally in terms of how we operate. It's different for everyone. I think all those rounds are ultimately just a representation of the idea size
Speaker 2:Totally.
Speaker 5:Right, of the opportunity.
Speaker 2:Yep.
Speaker 5:And then and then, of course, also of the entrepreneur and the team. Yeah. We did make, you know, one investment very early
Speaker 2:Mhmm.
Speaker 5:That that maybe fits this mandate, would be skilled in the robotics space, which has been a great company. It's executed incredibly well. And we Again,
Speaker 1:Luke Metros over
Speaker 2:there now. Oh, yeah. That's right. Oh, cool. Yeah.
Speaker 2:Yeah. We just know someone who's on the team, but not not one of the founders.
Speaker 1:That left Anne left Andrew all to, to join. It's Skild. Right? S k I l d. S
Speaker 8:k I l d. Yep.
Speaker 5:Yep. So that was that was one where we lean forward a little bit earlier than we would with with most things. But and again, that was on the back of just being very bullish on robotics
Speaker 2:Mhmm.
Speaker 5:On a ten, fifteen year time scale Yeah. And that team and where they sat in the stack. But, generally, we tend to wait a little bit to see a company be a bit more fleshed out, but it's always case specific.
Speaker 2:That's amazing. Well, thank you so much for coming on the show. Thanks for hanging out. Well, we will talk to you soon. Hope you have great rest of the day.
Speaker 2:And congratulations on the curse around.
Speaker 1:We'd love to see Spencer. Have a good one. Cheers. Goodbye.
Speaker 2:Up next, we have Tyler and Cameron Winklevoss. But first, let me tell you about Turbo Puffer, search every byte, serverless vector, and full text search built from first principles and object storage, fast, 10x, cheaper, and extremely scalable. The, the Winklevoss twins are in the restream waiting room, I believe.
Speaker 1:Let's If not,
Speaker 2:we have a slight delay. Let's follow-up on that. Back to the timeline. Back to the timeline. Dario Amade predicts that we will get to 90% on SWE bench verified in a year.
Speaker 2:That was one year ago. It's been one year. The best performing model, Sonnet four five with parallel compute gets 82% on SWE bench verified. Close to 90%, but not quite there. They put him in the truth zone.
Speaker 2:RIP to Dario. What do you think, Tyler? Bearish?
Speaker 4:I mean, I think he was pretty close. He's it was
Speaker 2:Oh, it's pretty close. Good enough these days?
Speaker 4:That's that was way more of a bullish take than most people.
Speaker 2:Yeah. No. It is it is very impressive. Totally. Do you think it's saturated?
Speaker 2:People are saying that that
Speaker 4:Yeah. Yeah. I I think a lot of those kinds of benchmarks are generally saturated in there.
Speaker 2:Yeah. Yeah. Yeah. Yeah. I mean, it is interesting that, like, simultaneously, you hit the 90% on Suitebench.
Speaker 2:Did very well there. But on the flip side, you have Audra Carpathi saying, like, it's slop, and, like, you can't actually use it for, like, the frontier software development that he wants to do. Anyway, I believe we have the Winklevoss twins in the restroom video. Let's bring them into the TVP at Ultradome. Tyler and Cameron Winklevoss, welcome to the show.
Speaker 2:How are
Speaker 1:you guys doing? On, guys? Hey, guys.
Speaker 2:Very nice background. Give us the update. Give us the news. I know we're running late, so let's just jump right into it. I assume everyone knows who you are.
Speaker 9:Cool. So we launched Cypherpunk, a Zcash DAT yesterday. Mhmm. It trades under CYPH, c y p h. Mhmm.
Speaker 9:We're really excited about it. The the mission of the company is privacy and self sovereignty starting by accumulating Zcash. Mhmm. And then in due time, we hope to invest in other technologies that promote privacy and self sovereignty.
Speaker 2:Delay is extremely exciting.
Speaker 1:Having some Wi Fi issues. Sorry. Say more about the structure. This is Leap Therapeutics. You guys have rebranded it.
Speaker 1:This was an existing public company, but what more can you say on how this came together?
Speaker 9:Yeah. So it's an existing biotech company, and we basically took it over, and invested via PIPE into it and then, rebranded and changed the ticker today.
Speaker 1:Mhmm. Awesome. So I guess Zcash has had a lot of momentum recently. At the same time, a lot of the DATs have have, you know, struggled in in more recently. What like, talk through kind of, like, the next how how you're thinking about, you know, making sure just kind of navigating this time when the markets are are pretty choppy overall.
Speaker 9:Sure. So I think number one, the long term thesis we really believe in. And I think that when you look at Bitcoin as a store of value or how you store your value, Zcash is really how you move your value. This is is very sound, and we're obviously very bullish on that. But in addition, we're the largest investor into the DAT.
Speaker 9:So we don't have, like, a lot of fast capital that's looking for a trade. We're just long term hodlers or in the case of Zcash, zodlers. And we just you know, we plan to to hold for for a very long time. And I think that's one of the differences is that other DATs have, you know, had fast term money and people that are moving in and out of it. And that's why we didn't fill the pipe up.
Speaker 9:We took, you know, the vast majority of it. I put a $52,000,000 check-in. So the vast majority of equity will not be trading out of this out of this, out of the shares.
Speaker 2:Mhmm. That makes sense. Well, thank you for the update. Sorry about the Wi Fi connection. We have some technical problems.
Speaker 2:We'd love to have you back on the show and talk more about what's going on in your world because, there's so many interesting projects that you're working on. But really appreciate you taking the time to give us a quick update on Cypherpunk Technologies, and congratulations on the on the pipe closing the rebranding every all the progress. So have a great rest of your day.
Speaker 1:Yeah. Thanks for joining, guys.
Speaker 2:Thanks, guys. Cheers. Let me tell you about Google AI Studio. Create an AI powered app faster than ever. Gemini understands the capabilities you need to you need and automatically wires them up, the right models and APIs for you.
Speaker 2:Get started at a i.studio slash Google.
Speaker 1:Building AI agents to to make the Wi Fi work?
Speaker 2:That would be cool. It would be cool if we could deploy an AI agent. I mean, I guess that's, like, an an, the next generation of the Restream waiting room. Like, the Restream waiting room, somebody talks to them in the in a different waiting room and and checks the the Wi Fi. That will be something.
Speaker 2:Maybe it's a 2026 project. Maybe it's a 2046 project. Who knows?
Speaker 1:Let's see. That could be the final boss.
Speaker 2:Hopefully, our next guest is dialed in to the Restream waiting room. We have Max Hodak from Science. How you doing? What's going on? Welcome to the show.
Speaker 10:Hi, guys. Good to be here.
Speaker 2:To meet you.
Speaker 1:Great to have you.
Speaker 2:I believe we met, like, years ago at an Oppenheimer screening potentially. I don't know.
Speaker 10:Very possible. Very possible. Probably happened. Yeah.
Speaker 2:Anyway, for those who don't know you, please, kick us off with a little bit of an introduction on yourself and, and maybe the company as well.
Speaker 10:Sure. So, I mean, I've spent most of my life thinking about how to engineer the brain Mhmm. Build I mean, I origin story, I think, this field started when I was in the fifth grade and I saw the matrix. And I was like, I have no idea if we're living in a simulation, but we are definitely going to build one.
Speaker 2:Yeah.
Speaker 10:I'd kind of broadly characterized my ambition as to eventually disappear into the simulation, never to be found. And I, as an undergrad, talked my way into a lab that was doing neural recording in in primates and spent really, that was where most of my education happened. And then in 2016, I got pulled into cofounding what what became Neuralink, and I was there for four and a half years. And in the 2021, started this company, Science. We're now about a 180 people.
Speaker 10:Our main product is a retinal prosthesis. Like, really the first retinal prosthesis that really works for a sort of vision. It was in it's actually on the cover of Time last week, which is pretty crazy experience. Wow.
Speaker 2:So you like the matrix? Do you like the 13th Floor? Have you seen that movie?
Speaker 10:I have not seen the 13th Floor.
Speaker 2:Oh, the 13th Floor. It's like one of those movies that got terrible reviews, but it goes a little I feel like it goes a little bit farther than the matrix in terms of simulation theory and simulation hypothesis. It's a lot of fun. But, anyway, we can get back to the actual, story. So, how how far are you in building this business?
Speaker 2:Give us a give us a general update on, like, the shape of the business. It seems like you're in the office right now. How big is the company? What's the progress overall?
Speaker 10:Yeah. So we're the so we we've a couple different elements of our pipeline. So the the retinal prosthesis, we think, like, that is first, like, just an end in itself. Like, if you can restore vision to the blind, it is, like, it is a quest that humans have been on for thousands of years. That is an unsolved problem.
Speaker 10:And I think, like, we've made there's, like, real progress on that. There's we got two programs. One is a retinal chip called PRIMA, which is this little, it's a little chip implanted into the back of the eye under the retina that has all these light sensitive cells, works in conjunction with a laser, laser projection glasses worn by the patient. That finished a major clinical trial last summer. It was published just recently in the New England Journal of Medicine.
Speaker 10:These patients go from being unable to recognize faces. They can they can walk around because they've got a little bit of residual peripheral vision. The trial is in an age related macular degeneration, but they definitely can't read. They are really profoundly blind and disabled. And the the best patients in this trial could read could go from reading none none of an eye chart to reading the entire eye chart.
Speaker 10:Mean, there's videos of these patients like filling in crossword puzzles. It's really very cool. And then separately from that, we also have like, the another key program in the company is a different approach to building brain computer interfaces where instead of placing wires, like, not like, not in the retina, but, like, in in cortex Mhmm. Instead of placing wires or cables physically into the brain or genetically modifying the brain so that like, there's some things you can do with, like, with optogenetics or sonogenetics. Instead, what we do is we have we grow up these heavily engineered biological cells that we hide from the immune system.
Speaker 10:Mhmm. And then we just sit this on the surface of the brain. And and so we don't place anything into the brain itself, but what these cells do is they grow in and they wire up and they form new biological connections and they can form billions of them. And so I think, like, the way to understand this like, I think actually fairly direct reference for how to think
Speaker 1:about face
Speaker 2:right now. This is like
Speaker 6:So we so
Speaker 10:we called it a we called it a bio hybrid neural interface. Have you seen James Cameron's Avatar movies?
Speaker 2:Yes. Yes. Yes.
Speaker 10:Do you know the ponytails
Speaker 2:of the course. Of course. I know exactly where they're going.
Speaker 10:Plug into, like, their tree memory store or, like, their horses. So I think the question is, like, if you wanted to build a ultra high bandwidth neural interface, like, how would nature do this? Yeah. I think what nature would do is it would grow a a new cranial nerve that has, a USB port at the end. Yeah.
Speaker 10:And that I mean, that that is super cool research, but that is definitely a research project. And so the way the company's architected is that is gonna be paid for by the retinal prosthesis
Speaker 2:Sure.
Speaker 10:Which is a, like, very practical near term medical device. We hope to have we've submitted for marketing approval in Europe for that. We're going through the review process now. Yep. The FDA actually is being much slower.
Speaker 10:It it it's possible it won't reach American patients for a little bit longer, and that's kinda crazy that Europe is gonna get it first, but that's where we are.
Speaker 2:Yeah. That's rare. Heard you had a narrative violation, but exciting.
Speaker 10:Narrative violation for sure. Yeah. But so, yeah, hopefully, that'll be on market next summer making money, and that is big enough to pay for the rest.
Speaker 2:That makes sense. So why go with a a retinal prosthesis instead of cutting a hole in the skull and putting electrodes directly on the brain and trying to deliver the signal that way. There's a lot of BCI firms, obviously, you cofounded one, that that are are seem to be approaching the problem that way. Is that just like like like, do you do you have a particular, view on that? Is that just farther out and you're and this is a way to get to market faster, or is there something fundamentally, like, higher bandwidth about your approach?
Speaker 2:Like, what are the different technical trade offs?
Speaker 10:So BCI is not a product. BCI is a field.
Speaker 2:Okay.
Speaker 10:And there are many different types of BCIs for many different types of applications. Like, obviously, you can't go into the retina to decode, like, a video game controller out of
Speaker 2:the brain. Yeah. Yeah.
Speaker 10:Simultaneously, you can't stimulate, like, frontal cortex to do, like, to do some, like, sensory feedback. Mhmm. And so it really depends on the type of thing that you're trying to do. And when and so in vision, which we I mean, we think that a a visual prosthesis is a is a brain computer interface. We also think that cochlear implants are brain computer interfaces.
Speaker 2:Sure. Sure.
Speaker 10:Sure. You don't need to be drilling into the skull to get to cortex. So if you wanna restore vision, kind of have a choice of you've got the retina. The the output of the retina is the optic nerve that goes to a deep brain structure called the the thalamus, and then the thalamus connects up to cortex.
Speaker 2:Mhmm.
Speaker 10:And so within the retina so let's just take a look at, like, the options that you have here. So in the retina, normally, light shines in from the front. It hits the rods and cones. The rods and cones are the light sensitive cells. There's about a 150,000,000 of those.
Speaker 10:These connect to about a 100,000,000 intermediate cells, the bipolar cells, and those compress down to 1,500,000 get like, optic nerve cells. That connects to about 1,500,000 cells in the thalamus, and that connects up to, like, 200 to 500,000,000 cells in cortex. Mhmm. And so no one I mean, people have been trying to stimulate vision of the brain for many decades. And until the the clinical trial that we just finished, no one had ever gotten form vision, like structured vision that the brain could intuitively assemble into a whole.
Speaker 10:Like, you could get patterns that if you, like, looked at it carefully, it's like, oh, there's a good line here. There's a line here. It's, like, connected. That must be an a. Like, here's a line.
Speaker 10:Like, that's an n. But in the PRIMA trial, I mean, they could read off words at a time, and that had never really happened before. Right. And a big difference is that we're stimulating that first layer of cells, the bipolar cells in the retina. And we know that if you just go one layer deeper to the from the 100,000,000 bipolar cells to the 1,500,000 optic nerve cells, if you stimulate those optic nerve cells, you don't get this.
Speaker 10:You just get these diffuse flashes of light that you can't really attend to and the brain does not intuitively assemble together because the brain has already compressed the signal. And you have to then figure out, like, what is that transform? How did the brain compress this? Or how did even just the retina compress this? And the and so the thalamus has the same issues as trying to simulate the optic nerve, except it's under eight centimeters of brain tissue under the skull.
Speaker 10:And then once you're up in cortex, you're dealing with hundreds of millions of cells that are distributed over large areas that you just can't stimulate selectively. And so, like, people if you do this, like, you absolutely will get flashes of light. But converting that to, like, form vision that you can intuitively read is a totally different problem. Mhmm. And so and then even if that worked, like, even if that worked perfectly, one is, like, an outpatient surgery going through the soft tissue of the retina.
Speaker 10:The other is, like, a four or five hour brain surgery drilling through the skull. Mhmm. I think, like, one of those is you're gonna kinda win sales.
Speaker 2:Yeah. Wait. So you said outpatient. Walk me through comparing the level of intensity of the surgery to something like LASIK.
Speaker 10:So it's a little more than LASIK. Yeah. But it's, like, not a ton more than LASIK.
Speaker 2:What about getting your wisdom teeth out? I was just coughing. Yeah. I
Speaker 10:mean, you could do this. So in the trial, moaning of them ended up being done under general anesthesia.
Speaker 2:Okay.
Speaker 10:But to be honest, in these cases, general anesthesia is really, like, as much like a commitment mechanism for the surgeon and the patient than it is, like, there's any medical reason to do it. I mean, you just you can't change your mind halfway through.
Speaker 2:Oh, okay.
Speaker 10:Commitment mechanism. Wow. It's wild. You but from a, like, experience perspective, so you could do this with you can make an injection next to the eye. The eye goes dark and numb for a couple hours.
Speaker 10:Yeah. And the and then you can go in through the soft tissue of the eye. You leave the chip. There's a little injector. The surgeon presses a lever.
Speaker 10:It leaves the chip under the eye. Yeah. They come out. They're done. And so it's a really very simple
Speaker 2:Yeah. And then and then sorry. Just to be ultra clear, like, the chip is in the eye, then how am I communicating with the chip? Is it wirelessly? Is there is there a device that's on the other side of of my head?
Speaker 2:You said glasses maybe are interfacing with that?
Speaker 10:Yeah. So if you look at this chip under a microscope Mhmm. It it has all these little hex cells on it. And every one of these hex cells, like, the Science Prima chip, it's essentially a solar panel.
Speaker 2:Mhmm.
Speaker 10:And the it works in conjunction with there's glasses that are worn by the patient that has a camera looking out at the world, although you could really get the video feed from anywhere. Mhmm. And then there's a laser that projects the image onto the back of the eye in the in infrared.
Speaker 2:Okay.
Speaker 10:And because you can't see infrared, you can't like, if you have residual peripheral vision or any if you're not totally blind, this doesn't interfere with that. You can still have that. Mhmm. But the infrared laser where it strikes the implant, works it like an overhead projector. Like, wherever there's white that is projected, that is like, that's energy.
Speaker 10:And wherever there isn't energy, it's dark.
Speaker 2:Mhmm.
Speaker 10:And wherever the laser is absorbed by the implant, it it stimulates the cells directly above that pixel.
Speaker 8:Mhmm.
Speaker 10:And so this is pretty cool because the implant is powered by the information that's projected onto it, like like as a solar panel. This means that there's no implanted battery. There's no cable. Like, this tiny little fully wireless two millimeter chip is the whole thing.
Speaker 2:Mhmm.
Speaker 10:And also because the eye moves relative to the projector, like the projector is shining in from the front of the eye and then the eye moves and the image changes, this means that the brain can easily fuse it together with their their existing vision. Mhmm. And so this there's, like, some pretty cool stuff here. Like, if you show one of these patients a solid green bar all the way across their visual field, they'll see a contiguous bar even though the implant only fills, like, a small area of the total area of of blindness. And they'll say it it's like, it's green and then it turns white because we can only get black and white right now, and then it turns green again.
Speaker 10:But the brain fuses all of this together. And so even though the implant only has 400 electrodes, as the eye moves around, you don't you don't experience the image that falls on the eye or falls on the retina like a camera. The thing you experience is the brain activity of the world model in the brain. And so as the eye is moving around, it's updating the world model, and that's the thing that you see. And so even though it's, like, 400 electrodes, you can't think of it like 400 pixels on a screen.
Speaker 10:Yeah. You think of it as just getting the information to the world model and the eye is moving around and the brain's cross referencing all that. So it actually does significantly better than you'd think from being 400 electrodes.
Speaker 2:That's fascinating. How do you how do you think about the analogizing around the artificial intelligence community of what you know of the brain? So a lot of people in computer science or AI might say, with with LLMs, we've built this piece of the brain. With the hard drive, we built the long term memory. Has has what did your research have you mapped any of that onto the current state of artificial intelligence?
Speaker 2:Has it proven, insightful to help to for you to understand what's going on in AI?
Speaker 10:This is funny because, I mean, at the very beginning of of Neuralink and OpenAI, we were in the same building in San Francisco. And we'd have these discussions about, like, oh, who's gonna learn more? Is AI gonna learn from neuroscience, or is neuroscience gonna learn from AI?
Speaker 2:Yeah.
Speaker 10:And I think it has been revealed that, like, a guy asked I caught up with one of those guys a while ago, and I was like, oh, like, retrospect, what do you think AI learned from neuroscience? Mhmm. He thought about it for a second. He's like, the concept of a neuron.
Speaker 2:Yeah. That's it. I'm like,
Speaker 10:that's basically it.
Speaker 2:That's like Yeah. The very it's literally just in the name. Neuroscience is a neuron, and that's it. And then nothing else. Because, yeah, like the whole brain structure, that's it.
Speaker 2:Anyway, continue.
Speaker 10:And, but going the other way, I mean, AI has been so useful. I mean, there is a really interesting convergence going on. This is kinda called, like, neuro AI Okay. Where neuroscience is learning a lot from artificial intelligence in ways that I don't think any of us really would have anticipated.
Speaker 2:Okay. Explain.
Speaker 10:And have you come across the platonic representation hypothesis?
Speaker 2:Mm-mm.
Speaker 10:So there's an empirical finding that different neural networks trained with different architectures, different objectives, and different concrete datasets, But for the same type of thing, like images or or language or audio, they produce these, like, these similar internal representations.
Speaker 2:Mhmm.
Speaker 10:And what I mean by, like, rep like, there sometimes you'll hear people say, oh, these models are, like, stochastic parrots or they're just, neck. Like, there's glorified autocomplete. It's like Yeah. These people are safe to ignore. Like, the mathematical objects that you see appear inside these models are super interesting and look a lot like the representations that you see in the brain.
Speaker 10:Mhmm. And so that is hinting at, like, there's some, like, deeper fact about the universe that we're figuring out here that, basically, if you have a lot of compute power and you kind of run it in these ways, then you see these, like, these datas these, like, mathematical objects kind of emerge. And what evolution did and figured out in the brain is, like, looks a lot like stuff that you kind of see in in these transformers and these other AI models. And there's definitely some interesting unification happening there. Mhmm.
Speaker 10:I mean, it's not I it's still, like, it it it's a little more than speaking totally metaphorically, but it is still, like, I'd say, like, instructive rather than literal. But that's getting like, every month, there's, like, some new cool thing that comes out around this. I will say that
Speaker 2:I
Speaker 10:don't know. And I my view is that the transformer is, like, a reasonably good model of, like, what cortex is doing, but there's a lot more of the brain. And so there's other parts that aren't fully cap like, that aren't that we're gonna need something else, but it's not like the transformer is wrong. I think it's, like, probably part of the story.
Speaker 2:Yeah. That makes a lot of sense. I've, I I wonder what you think about, just the idea that, like, it, like, it takes, like, millions of years to train a a model to, like, drive a car, and it takes, you know, a 16 year old, like, a couple weekends to do it or, or the amount of energy that it I will consume in one day of, like, reasoning generating my own reasoning tokens is, like, way less than what it takes to run a data center. There seems to be some sort of, like, exchange ratio of, that's like, we're off by a couple orders of magnitude. Maybe that's an algorithmic question.
Speaker 2:I don't really know. Do you have any thoughts on that?
Speaker 10:Yeah. I mean, evolution has done I mean, it it is really it has minimized it's been very good at minimizing energy
Speaker 2:Mhmm.
Speaker 10:And optimizing some other stuff.
Speaker 2:Mhmm.
Speaker 10:But, I mean, that I think is, like, the advantage of of biological brains. Like, every now and then, I I see pitches from companies that they're saying, well, AI is really energy intensive. So what we should do is we should grow up cultures of biological neurons and train them to, like, to do intelligence tasks. Because you can kind of do this. Like, if you you can grow up neurons on electrode arrays, and you can condition them to learn things by stimulating them in different ways.
Speaker 10:Like, when I was in college, grew up a counterstrike game bot. Like, it's actually not that hard to do. Yeah. And and I and this is the thing that I think nerd snipes many people in this field at some point.
Speaker 2:Mhmm.
Speaker 10:But I I don't think that that's, like, the way to go. I think that's there's, like, just really structural advantages in silicon. Like, if you compare and contrast these two approaches Yeah. Like, in the in the deep learning models, like, you can see all the weights. You can introspect them.
Speaker 10:You can, like, stop the model. You can change one. You can replay it.
Speaker 2:Like
Speaker 10:Yeah. Samples about. You can copy to disk. You can, like, send it over the network. Whereas with the the biological living, like, these organoid brains, you like, you can't see the weights.
Speaker 10:You can't copy it to disk. It's like the what it learns is the time integrated experience it's always had. And, like, at some point, four or five months in, it will randomly get infected and die, and you'll have to start over. Yeah. And so I think there's just, like, structural it's, like, the thing that the biology does is it's energy efficient, but my response to this is, like, generate more power.
Speaker 2:Yeah. I like the idea of
Speaker 10:There's no such thing as a low energy wealthy society. Generate more power.
Speaker 2:That's yeah. I like the idea of How? The solution to AI is just like have kids or something. And you're like you you you reversed it all the way down to like, if I want artificial intelligence, I can do it biologically and just have kids.
Speaker 1:Are you and the team getting much leverage out of models today? Is it accelerating your progress? Or is it really just about having, like, deep domain expertise and being more obsessed with the problem than anyone else and hiring the smartest people in the world?
Speaker 10:The I interestingly, I think companies like this are really limited by infrastructure. And so, like, I one of the things that I got from my prior boss was I totally received the gospel of vertical integration.
Speaker 2:Mhmm.
Speaker 10:And the and we're really not it's not like we're held back by, like, genius scientific insights for the most part. We're limited by, like, oh, like, well, we need to get a new, like, a new material in our, like like, microfab deposition tool, but this requires hooking up some gas plumbing, which requires getting some specialist vendor to come out and, like, weld it to the machine, or you're out of animal housing and, like, building that is, like, an architectural design process and then permitting and then construction. Like, you're really limited by infrastructure more than your genius scientific insights. And so even if you had this like, I think when I think about, like, being in the takeoff era and what is gonna be the impact of these of progress in AI, like, we're still limited by how that can impact the real world in really meaningful ways for these ATOM's things. But I'd say there's two places that AI has had a bigger impact.
Speaker 10:The first is, ironically, like, comprehending regulatory standards and generating regulatory documents.
Speaker 2:About to say, you yeah. Yeah. Yeah. If if permitting's the bottleneck, can you have, like, a permitting agent that just goes and expands the permits until you get exactly what you need?
Speaker 10:Yeah. I mean, they're not quite like they you end up doing a lot of editing, but the well, he definitely just don't wanna spam the permits. This makes the regulators mad. But the but, I mean, the the filing in Europe for to ask for approval for a retinal prosthesis, which we submitted last summer
Speaker 2:Yeah.
Speaker 10:It was, I forget exactly how many. It was, like, tens of thousands of pages. Was a 65 gigabyte PDF. Yeah. And that depends on, like, hundreds and hundreds of standards that we're just expected to know all the details of.
Speaker 10:Yeah. And so being able to talk to these things is super useful. Like, chat with this these these datasets is super useful rather than, like, having, yeah, like, having a big team of
Speaker 2:Mhmm.
Speaker 10:Regulatory experts who are all kind of to use this through meetings. And then the other place that we've had big success with with AI internally is on our protein engineering program. So there's conventionally, many of the problems that we're interested in, you'd have to solve with, like, better electronics or better, like, mic micro like, better physical devices somehow. And we're now at a point where often when we find a problem, we ask, like, can we make a protein to solve this? So a couple months ago, we published a a paper on a new type of optogenetic protein, which is these are these are proteins that can make a a a neuron light sensitive that is not normally light sensitive, so we could control it optic with with light.
Speaker 10:And these typically are require very bright laser light in order to work. And we've we're able to use AI models to find one that is so sensitive that it is responsive to, like, not just daylight, but, like, indoor office lighting. Mhmm. And so that allows us to substantially reduce the power consumption so that we can because often the brain implants were often limited by thermals. Like, how much energy you can consume depends on how much is limited by how much you can heat the brain.
Speaker 10:So if you have more sensitive options, then you can have your LEDs be dimmer and have more of them. Then you can think about going from, like, thousands to hundreds of thousands. But then also that might turn actually into our next generation retinal product. We might. There that will have to go through clinical trials.
Speaker 10:That'll be a process. But it's possible that in five or seven years, you won't even need the chip at all. You'll just get an injection, and then we can just make the bipolar cells themselves light sensitive. And then that won't even need the glasses potentially. And that that really comes out of the the big breakthrough on on being able to solve these problems with proteins
Speaker 2:Mhmm.
Speaker 10:Is an AI enabled thing.
Speaker 1:Chat says we should have you ask how to how to explain this to preschool. Some labs, various groups have talked about the revenue opportunity and just automating science. And it's usually very general where they're just like, we're gonna come up with a bunch of ideas and then hypothetically, they give the ideas to various people to execute on and they get some type of like royalty on it. How much how much opportunity do you think there are for for a more general foundation model company to just come up with a bunch of ideas and then actually capture real value from it? It feels like, in some ways, the pharmaceutical industry maybe doesn't have a shortage of ideas.
Speaker 1:They have a shortage of infrastructure and funding in order to test enough ideas to actually get viable, you know, solutions?
Speaker 10:Yeah. I think that a real bottleneck here is just the translation to human subjects research and then to the market. It's really difficult. Mhmm. We have, like, the like, the thing I sometimes joke about is, like, we're in a golden age of mouse oncology.
Speaker 10:Like, if you're a mouse with a cancer, like, we've got some great things for you. But the
Speaker 2:I love that.
Speaker 10:Given like for
Speaker 1:all the mice out there.
Speaker 2:That's great to hear from the mice. Fantastic.
Speaker 10:Last time at least treated
Speaker 1:them for
Speaker 10:all that they've done for But, precise like, that's the trade off. And so if you can and the and I I get it. It's it's not you can't just say, like, oh, well, the FDA is the is the problem. Like, the problem like, problem is that human subjects research is, like, life or death. It's like it's a it's like no joke.
Speaker 2:Mhmm.
Speaker 10:And, like, I've had the experience of, like, a patient goes into a a new surgery for the first time, or they or you're gonna inject them with something and you're gonna wait. And, like, that is a very stressful experience. Like, you want them like and that's, like that's stressful experience for be like me, I'm not even the one getting it. Right? Yeah.
Speaker 10:Yeah. And so the there's trade offs there that I think are very deep in, like, our like, the way that we in our civilization, like, value human life, which I, like, I think is right. Mhmm. And that there and so you have this knob of, like, how much risk do you think of taking in human subjects research and how fast do you get new things? And we know that the toolbox of science is very, very powerful.
Speaker 10:Because when you look at what's possible in the animal models, you have these amazing things that are possible, but then getting that into humans is like and that and it's not just to say, like, oh, well, we like like, oh, we should just deregulate all of this. Like, there's it's more complicated than that. Although, I do think that there's a little bit of that. But at the same time, I mean, certainly, there's there's an intelligence effect. Like, I think that there's it's gonna be inevitable that these things I mean, the fact that you can fold all the proteins at least in static forms is a big deal.
Speaker 10:Like, I would not I would absolutely not bet against improvements in AI leading to improvements in in medicine and health care.
Speaker 2:Mhmm.
Speaker 10:And I think they're actually just to go one step further, think then the thing that we're gonna have to reckon with is health care. So, like, if if you thought, like like, twenty years ago, TVs and phones and computers were way, way, way more expensive. And now they're much cheaper, but we spend more on them total. Is this is like a technological growth industry. Like, normally, if NVIDIA sells 20% more GPUs next year and their revenue goes up and their earnings go up and the stock price goes up, like, everybody's stoked about this.
Speaker 10:But as time goes on and there's more things to spend money on in health care that produce better outcomes for longer lives, there's, like spending should increase. But because we pay for this sort of these, like, kind of insurance schemes, which are kind of pseudo fixed buckets of money, Like, if if there were real breakthroughs in health care that allowed people to live much longer and have much better outcomes, but they cost money, and you could spend, like, 10 times as much on health care, like, that would be a catastrophe. Mhmm. Like, you do not wanna spend like, right now, our system would not handle spending 10 times as much on health care.
Speaker 2:Yeah.
Speaker 10:But that is kind of directly at odds with it being a technological growth industry.
Speaker 2:Yeah. What about the BCI industry? I think it's interesting. Yeah. We've seen, like, this boom in quantum computing, and and there's public companies do all this stuff.
Speaker 2:But it feels like
Speaker 1:Well, so specifically, I feel I feel like the interesting question with BCIs is, like, I wanna understand your timeline. Yeah. People are It feels like there's these therapeutic use cases where somebody's blind and they they
Speaker 2:Yeah. Yeah.
Speaker 1:They're willing to take some level of risk in order to see or at least see something. And then there's like the utility timeline and entertainment timeline where I just have, you know, instead of wearing glasses, I just have a screen that's just embedded in my retina. And I don't need to wear glasses, and it's always on. And I can, you know, turn it off. And I I feel like that's where, like it feels like that's where you're going is sort of a general purpose technology on a long enough time horizon, but maybe I'm off.
Speaker 10:Yeah. I mean, I think I mean, it'd certainly be cool if the like, when your eyes are open, you've got the world of bit, like, world of atoms. And when your eyes are closed, you've got the world of bits. The I mean, the the near term is all these are medical devices, especially on the cortical side. I think these are very serious brain surgeries that, like, healthy 40 year olds are not gonna be like, if your hands work, your a keyboard is a really great brain computer interface.
Speaker 10:Yeah. And there's a lot of research that goes into, like, the design of the Xbox controller or the design of, the VR inputs. Because if you can convey the signals from your brain to the muscles, like, you can just capture that, and that works really well. But at the same time, I think many people eventually become patients. Like, these bodies are great until they're not.
Speaker 10:And as they start to fail, we should have better options. And so I I think that DCI I think that there's a kind of a meme that it is an AI adjacent story, but I actually think it's like a longevity adjacent story. I see, like, the, like, the only organ I really care about is the brain. As far as I'm concerned, kind of the rest of the body exists to, like, keep the brain alive and healthy and move it around and cause it to do things. And I'm gonna be fairly disappointed if I'm murdered by my pancreas or my heart.
Speaker 2:Oh, yeah.
Speaker 10:And so I think the you can get to this point where you say, like, well, instead of they've got all these hard problems, like, yeah, we've made progress on cancer and cardiovascular disease and metabolic disease. But, again, like, when you think about health insurance, like, you can't insure something with a 100% loss rate, and we still that is what we were talking about in medicine. Mhmm. And I think you can get to this point where the the brain is the thing that is really special and makes you you. And, like, when I look at a person, see an agent and a robot, and biology makes great robots.
Speaker 10:But the thing we really care about is the agent. And if you can kind of deal with the agent directly and the rest become swappable parts, then instead of needing to cure cancer, we might just be able to avoid this entirely. And that I kind of see as as one of the central elements or at least how I think about the promise of BCIs. And so I'd say, like, one of our one of the thing that I think could really be possible is, like, by 2035 it it won't be widespread. I think it'll take longer than that for sure.
Speaker 10:But by 2020 2035, can you offer patient number one the choice of, like, dying of pancreatic cancer or being inserted into the matrix?
Speaker 2:Yeah. What's the strongest steel 2035. You can offer for the anti transhumanism arguments? Because I've I've been I've been steeped in this exact Silicon Valley lore for basically my entire life. I've heard it articulated a bunch of different ways in media, video games, The Matrix.
Speaker 2:And yet I've I've also seen a lot of pushback. Bucket. There's a lot of yeah. There's a of pushback against the transhumanism stuff. What what what's been the what's been the strongest argument that's that that that you've you've had trouble debunking, if any?
Speaker 10:Yeah. I mean, I I don't really like the label transhumanism. It just has, like, all these, like, connotations. I also don't like to work the term like, the I think it is important that these things like, people don't wanna be different. Yeah.
Speaker 10:They wanna be themselves. Yeah. I think it's important to talk to realize that things we're talking about should make you kind of just as much you Mhmm. As you've ever been and just, like, with the best quality of life you've ever had.
Speaker 2:Mhmm.
Speaker 10:And I think, like, the cons a concern with AI is that we could it could be massively like, it could lead to really undermining our agency. It could be, like, lead to a massive loss of agency for humanity and the emergence of something else. Mhmm. Whereas I think that these BCI technologies are really about increasing human agency. It's really it's a very, like
Speaker 2:Yeah.
Speaker 10:Yeah. They kind of exist to facilitate you, and it should not and it should be it's not other than human. It's like it it is a very prohuman technology.
Speaker 2:Yeah. I mean,
Speaker 10:as much as any kind of health care. Right? Like, we we try like, you we do heart transplants. We get we're working on artificial hearts. You'd have dialysis.
Speaker 10:You have we treat cancer. We, like, think that these things are worth trying to improve on. And it's only transhumanist in the way that, like, any health care is. That you don't accept that just like the state of the art in the middle ages. If you're, like, playing in the wrong forest and got a scrape on a branch, you could suddenly die of a bacterial infection two days later.
Speaker 10:Like, we developed antibiotics. Like, this
Speaker 2:is
Speaker 10:only transhumanist in the way that antibiotics are. It's this continuation of that story.
Speaker 2:That's a great yeah. That's a great argument. I love it. What, yeah. Jordy, do you have anything else?
Speaker 2:I was gonna ask I was gonna ask about the merge.
Speaker 1:Yeah. I I'd be interested to get your dep personal definition of the merge. Sam Altman wrote in 2016 that it could be a scenario where people become best friends with the chatbot. Maybe this the merge is something like AGI where we just keep moving the goalposts.
Speaker 2:Yeah. Yeah. Or do do you think it's inevitable, or do think there's, like, some sort of fork? Or where do you think the unexplored discussion surface area is around the concept of the merge?
Speaker 10:I mean, the way that we interact with AI Mhmm. As I mean, there's many different ways that this could go. I think, like, the the failure mode of, like, terminator seems less likely. Mhmm. But I definitely agree with Sam that it like, an underrated failure mode is people just start, like, delegating their decision making to it because the models just make better decisions.
Speaker 10:Like, there's I was reading an article that one of The US military combatant commanders is now, like, running personal decisions by chat GPT.
Speaker 2:Yeah.
Speaker 10:And if you're just, like, realizing, like, hey. Like, these things make as good decisions as I make, then you they kind of come, like, pre merged. Like, you don't need a device for that necessarily because we are just kind of, like like, causing it to happen in the world, like, whatever it wanted. And there's, like, some dark versions of that. Like, you can imagine, like, an extension scenario is that we just really become we're like, man, these things have great judgment.
Speaker 10:They really know what to do. We should, like, ask it for advice and listen to it and, like, persuades a bunch of humans into suicide. Like, you should I think you gotta keep an eye on those rates. They won't be zero, and I think it's unreasonable to expect them to be zero because you're talking about hundreds of millions of people. But, like, do those trends like, what do those look like over time?
Speaker 2:Yes. Yes. Keep an eye on the rates. That's that's the great summation of what's going on. It it you should do the baseline's not zero, but if it starts climbing up, you gotta watch it.
Speaker 10:Exactly. Just like self driving cars. Like, humans are not perfect. Don't hold them to, like, un like, to these Yes. Unachievable standards.
Speaker 10:Yes. But, like, keep an eye on the rates.
Speaker 2:Keep an eye on the rates. I completely agree. That's a great take. Thank you so much for coming by the show. Congratulations on all the progress.
Speaker 2:Jordy, do have anything else?
Speaker 1:Yeah. This was incredible. I wish we had more time. Yeah. But, come back on whenever you want.
Speaker 2:Yeah. I really appreciate this. This is
Speaker 10:Thanks for having me on. Good to see you guys. This was super fun.
Speaker 2:Yeah. We'll talk to you soon. Cheers. Have a good one. Bye.
Speaker 2:Bye. Let me tell you about ProFound. Get your brand mentioned in chat GPT. Reach millions of consumers who are using AI to discover new products and brands. You get a demo at ProFound.
Speaker 2:Our next guest is Andrew Dudum from Hims and Hers. Two companies. No. Just one company. Hims and Hers.
Speaker 2:Welcome to the show, Andrew. How are you doing?
Speaker 7:I'm great, guys. How are doing?
Speaker 2:Thanks so much for taking the time to jump on the show. For those who who might not be familiar, can you, just kick us off with a little bit of an introduction on the state of the business right now? It's a public company.
Speaker 1:Okay.
Speaker 2:People know telehealth.
Speaker 1:Yeah. I thought you were gonna say for anybody that hasn't heard of of HIMSS because Yeah. I'd like to I'd like to find one. He
Speaker 7:has some great hair, so I feel like maybe we've got some customers on the call.
Speaker 2:Oh, okay. I see. Yes. Yeah.
Speaker 7:So Hims and Hers have been around for about eight years. It's actually our eighth birthday this month. Congratulations. Public in 2021. And the vision is to help people feel great through the power of better health.
Speaker 2:Yeah.
Speaker 7:So we connect you on your phone. You pick up your iPhone, you click an app, and you immediately get connected to one of a thousand or 2,000 doctors who registered in your state and then can immediately engage in everything from cardiovascular disease risk, at home testing for testosterone, menopause, weight loss prescription treatments, all the way down to stigmatized things like sexual health and mental health and depression and anxiety. So, you know, we treat millions of patients on the platform, 10,000 to 15,000 patients per day. So while we are in many ways a new and innovative telehealth company, as people call us, we're actually probably one of the largest healthcare systems in The US today, just given the pure volume of patients that we treat. And it's all digital.
Speaker 7:So no matter what zip code you're in, have access to world class consistent care, standardized care, which I think is one of the most important parts of all this digital health care revolution, which is no matter where you are, you get great care, as if you're ten minutes away from Stanford and go see a dermatologist, you know, right outside your door. So this year we're on track, you know, 2 and something billion in revenue profitable and growing super fast, which is exciting. I want you to know once we get categories.
Speaker 1:Hit it. Gong hit. Gong hit for you. Let's talk about labs.
Speaker 2:Yeah. Labs.
Speaker 7:Labs is an important one. We launched today two packets packages where you, for a few $100, can get over 120 biomarkers. This is the most advanced diagnostics, frankly, that are out there in the market. And I spend a ton of time figuring out with concierge doctors and others what is kind of at the leading edge, where you can get twice a year annual testing and then have all those biomarkers come into the platform and have thousands of doctors essentially overlook it, coupled with our AI, to be able to prescribe and treat very specific treatment plans for you. So it's not just a data dump of here's 120 biomarkers, good luck, throw it in chatty PT, but it's actually real providers that are then helping you make tactical personalized prescription next steps.
Speaker 7:So we have about a million square foot of pharmacy compounding in The US. So we actually make a lot of these medications ourselves. So if you're a person that comes in like me, low vitamin D, you know, testosterone's not as optimized as I want to be, so it's going be a zinc and ashwagandha or magnesium supplement along with core pharmaceuticals, let's say for heart disease like a statin, we'll be able to actually put that together for you in a form factor that you love, you know, customize into a beautiful personalized treatment, and then deliver it to your door for something like $30 or $40 a month. So it's really this vision of a completely unified system that gets to know you, optimizes you, and then actually can verticalize the making of treatments for you so that we can see you get better and help you kind of get preventative given we're in a country here where almost everybody dies of preventable disease, right? It's like heart disease, heart attacks, diabetes.
Speaker 7:It's stuff that takes like ten to twenty years to develop. We should be able to get ahead of this stuff and actually start people you know, help people change that course of direction.
Speaker 1:How do you how do you think the labs market evolves? There's a ton of players in this space. This is obviously not your core business, although it feeds into the core business. I think there's players that are trying to use labs as like a wedge to then go and compete with you guys. You guys are kind of doing the opposite.
Speaker 1:But do you think that margins on labs, like, effectively go to zero over
Speaker 7:time? It's go to zero. Yeah. I think anybody who's in the labs business today, whose core business is labs, needs a new business. Yeah.
Speaker 7:You've got Quest and LabCorp and great partners. They've got thousands of locations, you know, $1,015,000,000,000 dollar public companies each of them. There's no way that those margins will be constrained when you have a platform like ours where it's in our it's in our best interest, and it's in our consumers' best interest to verbalize this infrastructure over time and give it away for free. Right? For for you guys or anybody who's a Hims or Hers customer, it's to our benefit that we do a full panel and and get you that at cost or even as lead gen, right?
Speaker 7:Like, can we'll pay you to take it eventually as you actually verticalize this infrastructure because the data you then get back from it allows us to give you very tangible next steps and manage your care over, five, ten, fifteen years, which is really where the long term relationship and value creation comes from. So for us, we're excited about these price points. It's 199 for a base package. It's $4.99 for the advanced package. You know, my hope in three years from now, and my CFO would be mad if I said this, but it's like, my hope is it's $30.
Speaker 7:Right? And it just gets added on to your Hims and Hers member membership. And if you have a treatment with Hims and Hers at all, you get this stuff for free, right, because it just makes the platform and it makes your precision care that we can deliver so much better, right? Say you're a hair treatment and you're taking, you know, our oral chew for hair loss, which is like Finasteride and Minoxidil, and then we figure out that you're prediabetic. Right?
Speaker 7:Our ability to compound that with a low dose of metformin or some type of other, you know, micro dosing GLP-one or GIPP, like, that just transforms the actual clinical outcome for you, and it all started with a very simple relationship, which is I'm worried about my hair. I wanna start taking advantage of that.
Speaker 2:I wanna know your take on Theranos. I have this hot take that it was never a good idea at all because you can just take a full vial of blood, and it was even if it worked, it wouldn't be that big of a product because if I go to the doctor and they take this much blood instead of this much blood, it's just not that big of a deal. Do you think if Theranos had worked, that would be a successful product? Would that have reshaped? Like, was there ever a chance they would have been a great thing?
Speaker 7:Yeah. The only reason that the idea for Theranos is interesting was if you can do at home diagnostics as opposed to in office phlebotomy.
Speaker 2:Sure.
Speaker 7:Right? So, you know, that burden of leaving your house, going to a doctor's office, the number one fear in the country is needles, right? So you sitting there and somebody, you know, putting a needle in your arm to take out that tube of blood. Whether or not it's a tiny tube or a big tube, it actually doesn't matter. You know, you had to go and park and take time off work.
Speaker 2:I just see that as a terrible problem in our society that people are printing needles. Matter? We should not be fearful. I don't like that at all. I feel like we should solve the fear problem before we solve the needle problem.
Speaker 7:I think if you can do and and this is, you know, something we're working on, and we've talked about this. I think in the next year or two, you'll have at home diagnostic devices Mhmm. That you can put on your arm, click a button, you feel nothing. It's kind of like a CGM. Right?
Speaker 2:Sure. Sure.
Speaker 7:And you can actually probably eventually look at the interstitial fluid. So you're not even doing injections deep into the blood, but you're actually just doing kind of like top capillary fluids to be able to interpret what some of these base biomarkers are. If you can pull that off and it costs $10 to do that and you just click a button, take it off, and mail it back to HIMSS, that I think really transforms access because so many more people I think will do that versus the whole, you know, kid and poo caboodle of, like, going and scheduling and showing up in office.
Speaker 2:Yeah. Yeah. That makes a lot of sense.
Speaker 1:How what what's your, like, framework for how the GLP one market shapes out?
Speaker 7:Yeah. It's a crazy market. You know? It's, like, one of the the the most important categories probably in the next few years. I think you're gonna have more and more treatments on the market very quickly.
Speaker 7:Mhmm. Right? You've got Netsera deal with with Pfizer and Novo taking place, you know, back and forth. You've got Kylyra that just raised a huge growth fund that's got a GLPGIP that is at par or possibly superior to tirzepatide. You've got Viking coming out probably in '29 or '30.
Speaker 7:You then have Wegovy and Ozempic going generic sometime in 2030 or, you know, 'thirty one, which is gonna bring the price down to, you know, it costs, you know, it could probably cost $10 to make one of those vines. Right? So I think in the next few years, the landscape is gonna be incredibly competitive. I think the prices are going to come down dramatically. This is something we're very excited by.
Speaker 7:When we first started putting these treatments out, the prices were $1,500 a month, but now they're down to about 150. I would guess in three years from now, you can be buying, you know, one of the best treatments for $50 a month. And at that point, it's gonna be just cash pay. Customers are gonna be able to use HSA, FSA, not have to deal with know, the pain in the ass of insurance, and then the massive market will expand pretty dramatically.
Speaker 2:Yeah. Yeah. You said you have, like, what, thousands of square feet of compounding, something like that, tens of thousands. Yeah.
Speaker 7:Yeah. We've got a million a million square feet.
Speaker 2:Million square feet.
Speaker 5:Yeah. And you
Speaker 2:I feel like that's been controversial in the past. There's been battles between you and maybe the FDA, maybe the the
Speaker 1:Just a little controversial.
Speaker 2:Yeah. I mean, I've ever we read about it in the Wall Street Is there are are you staying the path on compounding? Is that where you wanna be in a decade for is is compounding here to stay, or is there a world where you wind up partnering with the the the legacy pharma companies?
Speaker 7:I think it's both. Okay. Right? I think we will inevitably partner with a lot of these companies. You know, we have the largest distribution platform for therapeutics in The US for consumers.
Speaker 7:Right? So if you have a next generation treatment, we should be talking. Because if we want to get your medicine to a lot of people, you know, there's just no faster route to do so. So I think that's a very logical and natural thing to happen. And we've been doing things like this.
Speaker 7:We recently invested in CRAIL, which is one of the, I think, the most advanced early multi cancer detection blood test. Simple blood test can detect over one hundred cancers as early as stage one. I have every single person in my family take this test every year. More things like that are gonna come to market, and I think will come to the platform. But for compounding and specific, there's really clear guidelines with regard to FDA allowance of compounding.
Speaker 7:There's different types of pharmacies. They can do different types of things for very specific reasons, provider personalization, form factor, side effect management, etcetera. We're a public company. You know, our chief medical officer was the chief medical officer of Walgreens. Deb Ottore, who's on our board, who runs the risk committee, was the woman who wrote a lot of compounding legislation at the FDA for a decade.
Speaker 7:So I think we play very clearly by the rules. I think this is one of the first times in history that we're able to give this level of personalization to a lot of people. Right? Historically, this level of personalization only was possible for people that were spending $50,000 a year for concierge doctors and they could have treatments made for them. And I think it's our ambition to figure out how do you get that for the 1% to everybody.
Speaker 7:And so I think there's friction, without question in that system, but, the regulatory framework is is is very clear, and I think we're just staying the course. And and I think over time, we'll be able to figure out ways to to to make the other parties in the ecosystem feel like they're getting, you know, well well compensated, as well.
Speaker 2:Yep. Makes no sense.
Speaker 1:How would you describe your management style? The the, you know, the company's, up massively from, you know, the IPO. I feel like I started tracking HIMSS during the D2C era. Right? You guys were the the poster child of D2C, this meteoric rise.
Speaker 1:And then and then, obviously, you know, the the public markets have been wild. Yeah. I'm I'm curious like how how you navigate, you know, the the market internally, you know, even with the team. Right? It feels like you guys are very built for it.
Speaker 1:But what's your approach?
Speaker 7:Yeah. You know, when we I remember waking up one morning and and looking at my wife and the stock was $2.87. Like, we were trading at, I think, like point four times next year's revenue or something like that. It was it was crazy. So I think the team has a stomach of steel, you know, whether it's not $70 or $2.87.
Speaker 7:I think, you know, most of the executive team will retire with this company, including myself, because I think there's this incredible opportunity to figure out how you redefine what is a health care membership that every single one of us wants to buy. Like, it's got the cutting edge. It has the most you know, the best diagnostics. It's the most affordable. It's an Amazon Prime version of health care that you can afford.
Speaker 7:Right? It's like $500 a year. For $500 a year, why deal with insurance in this country when everybody's insured, but everybody has a high deductible plan with a $2,000 deductible, right? So while everyone is insured, nobody can actually get the benefits of insurance because they can't actually afford to use all the cash to then get the $1 of insurance. So I think our management team is it's not a very glitz and glamour team, right?
Speaker 7:Like, you're not seeing us out on TV twenty four seven. It's a really heads down team. It's a gritty team. It's a team that loves to build. We have a ton of fun together.
Speaker 7:And I think it's a team that genuinely focuses on what can be accomplished in a decade and not gonna be accomplished in the next quarter or two, which is why I think a lot of the investments we're making have a much longer time horizon. But, you know, as a as a founder that runs the company that has the high load stock, like, we have that privilege to be able to invest over that long period of time.
Speaker 1:That makes sense. What kind of horror stories have you heard about Chinese peptides? There's sort of a meme going on on acts of people talking about it. Obviously, some of the pricing coming out, people buying directly there, is pretty wild. What what are the what are the risks that people should be aware of?
Speaker 7:Yeah. You've you've gotta make sure, you know, the the pharmacy suppliers you're working with are good quality pharmacy suppliers. There's something called a certificate of analysis Mhmm. That, you know, high quality FDA oversight facilities in The US can deliver third party tested and validation. You can actually ask for this certificate.
Speaker 7:We give customers a certificate for any of our compounded products, which actually shows the independent laboratories saying this is exactly what, you know, we say it is. So I think you want to have organizations or pharmacies that have that type of coverage, that type of third party testing, you know, good manufacturing practices, and ideally have some type of oversight from, you know, state boards or FDA. I think there's a lot of stuff you can buy online that's, you know, truly not for human use, and they that as a way to kind of loophole around, but it, like, truly is not for human use. And, you know, I would encourage people to to try to stay away from that kind of stuff.
Speaker 1:Random question. I'm curious to get your take on it. Something like 1% of US GDP is is dialysis. Yeah. Doesn't necessarily seem like a problem for HIMSS to solve.
Speaker 1:But from everything you know about, you know, working in in in and around this industry, how do you think do you think it's a solvable problem, or is
Speaker 7:it just a Yeah. You know, my grandfather was on dialysis for, like, the this five or seven years before he died, and it's it's like the worst thing in the world, like watching somebody on that. Right? You have to show up to the facility every week. If you don't, you literally your body deteriorates and you die.
Speaker 7:Dialysis is a result of you having totally failed taking action on you being sick for twenty plus years, like a very long time. Similar to cardiovascular disease, right? Like you show up in the ER at 60 and you have a heart attack. You know, that took twenty years to build up. Like, there's just no other way it happens.
Speaker 7:And so something like what we launched today I actually think is the first step of the solve because you get for a couple $100 something like a lipoprotein little a test, which tells you your predisposition for advanced cardiovascular disease, which means even if your cholesterol numbers are amazing, they're not amazing enough. Right? You have to have like the best gold standard cholesterol numbers like you're an 11 year old kid to avoid a heart attack because you have this predisposition to risk. Same thing with diabetes, right? Diabetes, A1C, fasting insulin, all of the, or fasting glucose, these all rise over time.
Speaker 7:So if we're 30 or 35 and have these numbers, you can just chart what it's going to look like at 50. And so, you know, micro steps in health, in food, in movement, in, you know, metformin at the right time or GLPs or preventative statins or PSK9 inhibitors. Like all of these tools exist. And for the most part, tools are not extremely expensive, right? So the real burden in The US healthcare system isn't actually lack of innovation.
Speaker 7:It's lack of education and access. So how do you get more people tested faster? How do you make it easy and less scary for them to take that first step? And then keep them motivated along the way because, you know, these treatments don't make you feel amazing the next day, but they'll save your life twenty years from now. And I think that's where a brand like ours spends a shitload of time is you actually have to love hims and hers because staying on this medicine is good for your health, and it's hard because you drop that habit and then you drop that benefit.
Speaker 1:Makes a lot of sense. Well, thank you so much for joining. It's great great to finally meet. Certainly, we're we're an inspiration during, again, that D2C era. Good.
Speaker 1:It was like just a crazy, crazy moment in in time, and it's awesome to see you guys execute in the public markets.
Speaker 2:Yeah. Congrats.
Speaker 7:Thank you, guys. Happy on.
Speaker 2:We'll talk to you soon. Cheers. Have a good one.
Speaker 7:See you.
Speaker 2:Before we move on, let me tell you about Linear. Linear is a purpose built tool for planning and building products, meet the system for modern software development, streamline issues, projects, and product road maps. We have our next guest in the studio, Melissa Tocamac, Tocamac from Netic. We also, the market is selling off like crazy. People are asking us to cover it, but we will talk to Melissa first.
Speaker 2:How are you doing? Hello. Good to see you. Have a seat right there. We do need to cover.
Speaker 2:I'm glad we have you on the show because, the market is in turmoil, but your business is maybe less indexed to the market. Is that correct? That is correct. Explain explain, you know, how, explain, the the business, the news. Take us through it.
Speaker 2:We'll we'll get your take on all sorts
Speaker 3:of things. Well, I mean, we serve the essential services businesses that are really backbone of our American economy.
Speaker 2:By nature, counter cyclical. Let's give it up
Speaker 1:for the backbone.
Speaker 2:The backbone of the American economy. Thank you. Okay.
Speaker 3:So these businesses are, these enterprises are in industries like HVAC, plumbing, electric, solar, consumer health, energy across the board. Whether the market is going down or up, that somebody, a business or a consumer always needs Actually, these today I'm coming from San Francisco as you know, all of San Francisco needs a lot of these services today because the weather is a complete No, has been it's been complete rain. Everything is shut down. Yeah, electricity goes off all the time and I'm sure a lot in the Bay Area will be needing these services all So, day we do is provide really these enterprises with an AI revenue engine. Right?
Speaker 3:So, in these times of need Mhmm. They can actually handle all of the demand Mhmm. With AI agents and serve the communities. Yep. But also, when the demand is soft, they can predict the need Mhmm.
Speaker 3:And turn these relationships into multi time recurring relationships.
Speaker 2:Okay. Yeah.
Speaker 1:So I I feel like every time I call a plumber or a roofer or an electrician, people like that, they don't like, the delay and the lag of getting like, there's always, like, crazy lag in in terms of getting back and just being like, yes, I can see I can come by at this time. Mhmm. And I feel like the lag is because they're super busy. Yeah. And if you can help them respond more quickly, they can generate a lot more
Speaker 3:Actually, many of them are very large businesses Mhmm. And the lag is because everybody needs it. When you need it, odds are millions of other people need it the same And I'm sure, like this has been talked to you in this program too, there's a national shortage in skilled labor
Speaker 2:Sure.
Speaker 3:In these industries too. So it's actually, I mean Jensen talks about it a lot. Yeah. And it is the type of labor we need in the country. Yeah.
Speaker 3:And until then, they're
Speaker 2:also about was talking about. I feel like he said he like like, we, you know, we don't have enough plumbers, but I felt for some reason he was talking about, like, plumbing in data centers. Like Yeah.
Speaker 3:It's the same. Right? So HVAC technicians or plumbers. What the data centers need, Cooling. Yeah.
Speaker 3:Not that many. Or to be able to stand it up. But if you think about it, it takes a lot of years Yeah. To get actually trained for these jobs.
Speaker 2:Sure, sure,
Speaker 3:Probably more than I went to at school Stanford or you went to school for. It's very important and then afterwards you have to do it in real life the training as well.
Speaker 2:Sure, sure.
Speaker 3:So with data centers and consumers and businesses across the world
Speaker 2:Mhmm.
Speaker 3:The need for these businesses a lot. And it's not only about HVAC, plumbing, electric, when you say essential services, right? It's also across energy and solar or consumer health like the bake club, right, that we have been serving. So it's really the things that everybody in their daily lives
Speaker 2:Mhmm.
Speaker 3:Need and has to interact with.
Speaker 2:Yeah. Walk through the typical stack of HVAC repairmen. I mean, I imagine somebody could just have, you know, like a Yelp page and a Venmo account. Then some of them have a full website with a booking system and, almost like their own little mini ERP, probably not something they built themselves. Maybe they have a Shopify site or something.
Speaker 2:And then at a certain point, they get a roll up happens. They get bigger. They get more industrial. They get more mid market. And then I imagine that there's some sort of central, you know, point of record.
Speaker 2:Are you plugging into that? Are you trying to replace that? Are you trying to be the first the first choice for setting up every all the touch points?
Speaker 3:Yeah. So we primarily work with what you described as mid market and large enterprise. A lot of these businesses are owned by private equity
Speaker 2:Sure.
Speaker 3:And or still owner operated. Owner Owner they have built it from zero to hundreds of millions of dollars in revenue. They will actually I love that you went through that stack. They might have a more sophisticated tech stack than here. Sure.
Speaker 3:Right? So these businesses run on EBITDA. Right? Okay. So it's extremely important that they think about their efficiency, the customer relationship and how do they serve their communities.
Speaker 3:So primarily it will focus on you know what type of data they have and where do they keep it. There is software solutions and of course third party aggregators where they might do advertising. So we will partner with different solutions that they might have to take make benefit of any of the data they can. But primarily what we're doing is bring frontier AI to these industries so that when their demand is very high instead of losing that, they can handle it all at the same time. And when the demand is soft, they can generate net new demand, As you know, these industries just like we talked about San Francisco today is very volatile due to seasonality or some other external that they can't really control.
Speaker 3:Right? So that's why it's very important how you need to be efficient when the demand is coming, you have to capture it all.
Speaker 2:Yeah.
Speaker 3:And when it is not, you're thinking about how about brand that you
Speaker 1:can't sell during Black Friday, during Q4 you're cooks, like you're probably not profitable Exactly.
Speaker 3:Or even how to predict the next thing. Like one of our customers, there were tornadoes in Missouri a few months back and before the tornado literally happened using Netic, right, reached out to potentially affected areas and talked about generators. How would you feel if your electricity went in the Oh, you would not like that because your business would be down, right?
Speaker 2:Yeah, yeah.
Speaker 3:And but then afterwards I
Speaker 1:tried to get a generator when power was out
Speaker 3:last It was denied by It
Speaker 1:doesn't work well.
Speaker 2:It doesn't work once the crisis Once
Speaker 4:the the electricians
Speaker 1:are like, yeah buddy, I got 200 people calling me Calling you right right now. Yeah.
Speaker 3:I will help you out.
Speaker 1:Yeah. I got one. I'm covered now.
Speaker 3:Yeah. But also you know when the disaster actually happened, in ninety minutes maybe they got thousands of calls, right? So how do you help? But it was great for them because they could answer every call with Netc and they had already maybe rescheduled the non essential jobs that they had so that they could help the community in that moment. So that type of proactivity and focusing on the future and like revenue generating interactions for the businesses is very important for these
Speaker 1:Talk about traction? Yeah.
Speaker 3:Sure. Yes.
Speaker 1:You raised a new round?
Speaker 3:I did. Today
Speaker 2:Very we announced very
Speaker 4:low dilution.
Speaker 1:Let's let let's let her hit the yeah. As hard as you as hard as can.
Speaker 2:Hard as dollar series b led by Founders Fund. Hit that gong.
Speaker 1:Authority. At
Speaker 2:a 45 Powerful. $450,000,000 cap, which is a strong four x step up in valuation.
Speaker 1:Very low dilution.
Speaker 2:Very low Yeah. A lot. Yeah. Why?
Speaker 3:Well, I think from the beginning, I You really
Speaker 2:hear this? Good omen. Still going. When it still goes,
Speaker 3:it's same strength we put into our business. Exactly. No. I mean, that is actually the answer. From the beginning, what was important to me is that we build a business with strong fundamentals.
Speaker 2:Mhmm.
Speaker 3:Right? It is not about the hype, it's not For about the me, the most important thing even in investments, we work with the best and we're lucky and grateful that same people Yeah. Tripled down in a row in Nettick. And when you have a business with strong fundamentals and strong margins and scaling efficiently, you don't want to raise more than you need. And if you do that, by the way, that would be a detractor actually in the type of talent that we're hiring today.
Speaker 3:They do want to work in nimble teams. They do want to run through walls. They wanna be here because they want to build the future of I wanna
Speaker 2:through a wall.
Speaker 3:Yes, you should.
Speaker 1:Get one of the one of your customers to build us a wall. We'll run through it.
Speaker 2:Exactly. I have a question about models. Yeah. We we we were talking to Brian Chesky, and he's starting to add features to Airbnb that that allow the booking of, like, a chef. It's a very different it's a very different, model, for someone who's traveling.
Speaker 2:They want a private chef or something like that. But he mentioned that he's having a lot of success with open source models. Obviously, he's operating at massive scale. Yeah. And so every dollar counts.
Speaker 2:Are you seeing luck with open source models, or are you sticking to the the closed source models?
Speaker 3:Yeah. You actually can't stick to one. Yeah. The way to because if you think about it, maybe the difference there is we serve essential services industries. I'm like a utility to these companies.
Speaker 3:Can't go down.
Speaker 2:Okay.
Speaker 3:I can't be wrong. Yep. So the way that, so we build our own ML orchestration, right? The way that you build and think about models and what's really helping with each task that you need to do in that orchestration is very important. So, you have to think about what's best fit in terms of address verification in this case Sure.
Speaker 3:Versus understanding what does Jordy need
Speaker 2:Yep.
Speaker 3:When he's calling me, Or messaging me. So that would be need finding. So the whole point is how do you think of these modules and what do they need to get done and what is the model that can give you the best answer, right? And you have to obviously build a lot on your internal evals to be tracking that continuously and make any changes as you need, So today, definitely like we do use quite a bit of more closed source models. But depending on your evals, if something is not Mhmm.
Speaker 3:You know like keeping up with what we need and the new functionality we add, we would always test and think about any other model.
Speaker 1:What about ten years?
Speaker 2:You think in ten years you'd be
Speaker 1:Before that, are you are you using voice models at all? Like like you have like Yeah. We don't
Speaker 3:do voice to voice. We do have voice actually. Today, we support our customers from voice, text, online Yeah. Like any type of channel that you want. Are.
Speaker 3:We're the single inbox Yeah. Right? For anything that they're really getting. So voice, yes, we support. But voice to voice is actually not there yet, speech to So we do really orchestrate all of that in terms of speech to text reasoning models and then text to speech afterwards to really give that accuracy, reliability as well as the flow, the feel.
Speaker 3:Yeah. Right? And hopefully maybe in ten years you're asking, we will have to think think that not in ten years, maybe even a few, where's voice to voice, speech to speech is going. So according to that, we have to
Speaker 1:cycle like?
Speaker 3:To be frank with you, I think we really sell based on ROI. So we show our customers. Right? And they can talk to our existing customers at any point. Anyone interested, private equity firms that we support, large companies
Speaker 1:that we support. Firm's been one of the main channels in here where they're just like, hey, we have a we have a roll up of a bunch of different underlying businesses. Let's roll up a number of roll ups
Speaker 2:and we want the same software
Speaker 1:We have a roll up of roll ups.
Speaker 2:I mean, that's what private equity firms are these days.
Speaker 3:Today, yeah. There are roll ups
Speaker 1:and roll
Speaker 3:ups. That is correct. It has been one of the good channels. But also it can be direct to large companies itself. I think the reason we love working with private equity because in today's world, they understand the importance of AI and they're looking for an AI partner.
Speaker 2:Yeah.
Speaker 3:Right? It's not really So need
Speaker 1:an AI strategy, what can we buy?
Speaker 3:Yeah. Actually, you'd be surprised. Like, I think a lot of tech companies may be looking for something or a strategy to put on a board deck. A lot of these businesses I work with, I will say they're absolutely incredible and better entrepreneurs than entrepreneurs in Silicon Valley sometimes I see.
Speaker 2:They have
Speaker 1:experienced free cash flow.
Speaker 3:Yeah. It's I mean you can't hide behind raises and valuations, right? Like all you see is that does this help my revenue? Yeah, yeah. Very easy.
Speaker 3:And for me too, I can say, hey, I will not give you any random words, AI this, AI this, blah blah blah. No. Let me show you how is this going to affect your revenue. Yeah. Let's talk about that.
Speaker 3:So when you do it that way, I think the cycles are pretty fast.
Speaker 2:Yeah. It's gonna say exactly.
Speaker 1:Do you think people are scared to compete with you? You seem like a pretty formidable opponent.
Speaker 3:I don't know. I I hope not because I
Speaker 1:You enjoy crushing. Yeah.
Speaker 3:I do enjoy crushing. Crushing. Know? How are you me, my team too. And this is just for them from here.
Speaker 3:You all that are watching right now, you're all beasts. And there's no one else in this world that I would rather work with other than you. Incredible people coming from scale, HRT, Databricks, MIT, Stanford. I don't want to even talk about the accolades, who cares? They all most of them have deployed AI applications in the real world in production and they run through walls every day to deliver for the real world even though they could be anywhere.
Speaker 3:They could go in any company.
Speaker 1:Well, for the series c, we will get a wall built and you can run through everyone. Exactly.
Speaker 3:I will be doing that.
Speaker 2:Yeah. Thank you so much for coming on the show. Amazing progress. Congratulations. Thank you
Speaker 3:for having me.
Speaker 2:We'll talk to you soon. We have one more guest. Stay with us. The market is crashing. Everything is in turmoil, but the business continues.
Speaker 2:The show goes on.
Speaker 1:The White House the White House needs to announce a hundred year mortgages.
Speaker 2:Yeah. Yeah. Let's move to one hundred year mortgages. That will be the solution to all this. No.
Speaker 2:The only thing that can say this right now is numeral.com, sales tax, and autopilot. Spend less than five minutes per month on sales tax compliance. Also, fin.ai, the number one AI agent for customer service. Number one in performance benchmarks, number one in competitive bake offs, number one ranking on g two. And our next guests are here.
Speaker 2:Let's bring them into the TBPN UltraDome. We have Jeffrey Katzenberg and Tomas. Welcome to the show. Folks, thank you so much for taking the time to come down to the TBPN Ultradome. Good to see you again.
Speaker 2:First of congratulations on the news. Let's get some introductions first. Who are you? What organization are you with? Let's kick it off there.
Speaker 6:I'm Tomas Puig. I'm the CEO and founder of Alembic.
Speaker 2:Thank you.
Speaker 8:Jeffrey Katzenberg, GP at, Wonderco.
Speaker 2:Third time on the show, second time on the show, something like that. But first time on the show here. Yeah. Congratulations. And what's the news today?
Speaker 6:Oh, well, the news is is that we actually just raised a $145,000,000.
Speaker 2:Fantastic. Why don't you go hit that bomb? Please enjoy the yes. Yes. Yes.
Speaker 2:It's Woah. Around the world.
Speaker 1:We haven't seen that before.
Speaker 2:Different style. Different Different Okay.
Speaker 1:Was very aggressive ring.
Speaker 2:I like it.
Speaker 5:I like it. It's still ringing now.
Speaker 2:It has a nice sound. So take us through the business. How how are you pitching it these days?
Speaker 6:What's really interesting about is we do a little bit of a different thing in the AI space. We do causal AI. Mhmm. And really, what our executives that we work with in these Fortune 500 and Global two thousands wanna know is they wanna know every chain reaction and lever that moves the metrics that they care about in the business at any time.
Speaker 2:Mhmm.
Speaker 6:And so where we started originally was on the marketing and sales side.
Speaker 8:Mhmm.
Speaker 6:And so they would be like, hey, I spent a $100,000,000 on a stadium name. All these unknowables, these large content pieces, all this brand, we all know it worked at the time. Mhmm. But nobody could actually prove down to the dollar, down to the actual effect of what it would be. And so when we built the company, originally, we were, you wanna know how to shine a light inside this black box Mhmm.
Speaker 6:And actually be able to get the real dollar value Mhmm. So that you can start telling stories, because there's been so much of this improvement in, say, the programmatic side of the house Totally. Buying these ads. Well, what ended up happening is once we built that, we found out it actually worked really, really well, and we got a lot of incredible clients. And then they started to ask us to do other things, like being like, oh, hey, now can you see how all that affects foot traffic?
Speaker 6:Now can you see how that affects my ordering systems? And as we started putting this causal model out further and further, we realized it actually worked on an incredible amount of stuff. Mhmm. And so then our clients started asking us to be like, well, can you do our FP and A for finance? Can you do our supply chain?
Speaker 6:And so that's really kind of where the business came from. But the core of it is is that, like, when you have low information Mhmm. Or low amounts of stuff, you wanna be able to actually affect the metrics you care about.
Speaker 2:Mhmm. Take us through some of the case studies. Who have you worked with? What's the most concrete example of you you talked about buying a stadium. Have you literally found whether or not crypto.comarena penciled out?
Speaker 2:Have you have you looked at these? You know, I grew up at the Staples Center. I I I miss Staples. It's been renamed. But but I want to at least the red know
Speaker 6:stapler that
Speaker 2:likes I want to at least know that that it was worth it financially. Is it was it worth it financially? Can you tell me that?
Speaker 6:Yeah. So we did a
Speaker 1:Let's check Staples stocks and
Speaker 2:say, oh, oh. Yes. Should Staples have stuck with the sponsorship
Speaker 6:account for execution risk. Yes. Yes. Yes. When we
Speaker 2:There's there's always unknowables, but but what can you know?
Speaker 6:No. So we did a great case study with Delta Airlines Okay. That we actually presented with their CMO Okay. At the the NVIDIA conference, the GTC conference Sure. Where they were sponsoring the Olympics.
Speaker 6:Yeah. Yeah. And one of the things about these large sponsorships is there's two big aspects people don't talk about. One is every time you've ever worked in the business side of house, they go, hey, you need enough historical data to be able to do something.
Speaker 2:Yeah.
Speaker 6:Right? The second is it takes a lot of time to get a response. Mhmm. Well, when they were doing this when they were doing the Olympics, they were doing the promotions, one of the most interesting things we found out is we analyzed all this ad work that's coming out. You have only two weeks.
Speaker 6:You're holding up tens of millions of dollars on the P and L line while you're doing this. Right? These are not cheap options. And when we pulled the study, we actually found out that the best piece of content that functioned for them, the most profitable, was not actually the content that was the ads. The ads did okay.
Speaker 6:Right? The thirty second, sixty second spots. But what ended up happening is they actually if you watch the Olympics, they had the Delta Medal Presentation ceremony, where, like, every time an American athlete would win a medal, they would take it, put it on, you know, their shoulders, and that would be, you know, a really emotional moment. Well, it may seem kind of obvious in hindsight, but when you have the Eiffel Tower in the background, really emotional moments, you sell a lot of tickets to
Speaker 10:Paris. Mhmm.
Speaker 6:But the key is, if you can know that within a few days, you can either double down, right, you can sponsor the next Olympics. Yeah. You can do all these things, and then you can actually act on it. Now, the problem also with when you do big huge campaigns like that is you walk into the lounge
Speaker 2:Mhmm.
Speaker 6:For Delta Airlines, and Team USA is the WiFi passcode. It's not like one simple campaign. Right? You pivot an entire company to a messaging set. But we were able to tell them within a couple days down to the dollar a material amount of cash that they were able to pull back, and then they could actually show that to, you know, executives.
Speaker 1:So so problem in advertising, right, of like, know my advertise like, half of my advertising
Speaker 8:That's a line. I'm a percent Yep. Sure that 50% of my brand marketing is working if I only knew which 50%. Exactly. Which has been true in brand marketing forever going back to the nineteen eighties.
Speaker 2:Yeah. I'm
Speaker 1:sure in entertainment especially because there's less like you the attribution is really challenging. We did a bunch of marketing and then people went to movie theaters and then some people streamed it and it's hard to actually track it all the way through.
Speaker 8:But what the the brilliance of what Tomas and Olepek have done is is that using well, first of all, and he should explain it because he's the brainiac here. The new math Mhmm. That came out of contact tracing Mhmm. From COVID Mhmm. Which actually has impacted many different businesses in terms of research and, you know, causality and attribution.
Speaker 1:So we got advertising learnings out of
Speaker 8:Yes.
Speaker 1:Contact tracing.
Speaker 8:By the way Let's go. It's absolutely
Speaker 2:There's no
Speaker 8:it's a biggie. Very
Speaker 1:American to to like, you
Speaker 8:know entrepreneur says, oh, okay.
Speaker 1:Into an But
Speaker 2:That's great.
Speaker 8:When you think about it is is that either for that for COVID testing or for any of these attributions, what
Speaker 2:Yes.
Speaker 8:They are able to do, which was not possible even only three or four years ago, which is to be able to ingest Mhmm. Billions of rows of data Yeah. From all sources Yeah. And using machine learning and AI to actually digest that and make sense of that and actually then be able to see Yeah. Directional in in time.
Speaker 8:Yeah. You know, it's they'll he'll explain neural networks and how this actually works, which as I said is way above my pay grade here and it But I can tell you, which is to your to your question here, which is when we went to the top brand marketers Mhmm. The first reaction was, no way. Yeah. You know, this is like That's not possible.
Speaker 8:You know, yeah, this is not possible, and this is he was like telling us, you know, you know, pot of gold at the end of the rainbow or Yeah. You know, Dumbo flies or I don't know, whatever you you wanna say. And they went, not not possible. And that's when these guys would come in and do these POCs.
Speaker 2:Yeah.
Speaker 8:Disney, Mars, Accenture, Delta. And you know, when the world once is an accident, twice is a coincidence, three times you go, okay, well Mhmm. This thing actually delivers as fanciful as the notion may be. It's real.
Speaker 2:Yeah. Can you tell me a little bit of the history of how, like, product placement works in Hollywood? Because that feels like the classic example of difficult to measure, but you've been Like, We put this car
Speaker 1:in this
Speaker 8:one scene. Yeah. But it's but it's when you start to actually think about this, there's a trillion dollars a year Sure. Globally Yeah. Spent on brand marketing.
Speaker 2:Yeah.
Speaker 8:And brand marketing is everything from crypto on the Staples Center.
Speaker 7:Sure. Sure.
Speaker 2:Yeah. I I I
Speaker 8:to an interstitial
Speaker 2:Yeah.
Speaker 8:To imagine in the Disney Parks, all the things that you're you know, that they're able to offer to their brand partners to putting a logo on a car. What do you what you know, what's the value of having Oracle
Speaker 2:Yeah. On the race car.
Speaker 8:On a race car in this. On
Speaker 2:a bet.
Speaker 8:Or or a patch on a base on a baseball player or a basketball player, you know.
Speaker 2:So is it has it always just been intuitive? Yes. Or has it been more
Speaker 8:relationship driven? No. It literally is intuitive. It is I mean, he can tell you there's Yeah. There's a there's an old math.
Speaker 8:Mhmm. He'll he can Sure. Again, Thomas can really take you through explaining it. And that literally dates back to the nineteen eighties. And one, it's directional Mhmm.
Speaker 8:Not specific Sure. And two, the lag Yeah. Between the time it happens and you're able to gather the data on
Speaker 2:it Yeah.
Speaker 8:Yeah. Was months. Yeah. So it doesn't the value of it, you know, is really questionable.
Speaker 2:I I guess my question for you is it it it's very clear that with the with the progress in AI, there's the ability to find insights in data. Like, clearly, we see this across everything. My question is, like, there whether there's so little ground truth that how can your clients a b test your solution against something else? Like, if I go to a, you know, a a coding agent and I ask you to generate some code, I can run the code at the end and I can say, well, yeah, the code worked. Right?
Speaker 2:Or I can read the report. But if I go to you and and I say, how much was this sponsorship worth? And you tell me it's $4,000,000. And I say, okay. Like, maybe it was 4.
Speaker 2:I I that that could have just been a guess. So how do you justify the the results that you spit out since we can't run a controlled trial?
Speaker 6:Validation is one of the number one things we get asked constantly. When we give out papers, it's really funny. We have one paper where it's like a two page brochure, and then the next 15 pages is how we validate.
Speaker 2:Yeah. Right? Exactly.
Speaker 6:And, you know, one of the things that people don't talk about is it's actually very possible to test these things. Oh. So what you can actually do is you can do what we call back fit testing. You could set the machine backwards in time. Mhmm.
Speaker 6:And then you can test against things that you know. Withhold information. Mhmm. You can do all sorts of things to actually see whether, you know, you you feed the machine up till, like, last year. Yeah.
Speaker 6:You know what all the results are gonna be this year, so when you predict what something's gonna be, you can see how close you got. And so I think that when we're testing things, we use a variety of both synthetic data testing methods.
Speaker 2:Yeah.
Speaker 6:Because we're not you have to keep in mind, we're not a transformer modeler in LLM.
Speaker 2:Sure.
Speaker 6:It's an entirely new methodology. Yeah. And so when we grab this stuff, we have both synthetic data we can build. Yeah. So there's systems like for the nerds, TigraBite that does causal synthetic data.
Speaker 6:Mhmm. Or there's real data, like e f m r I data that we actually know that there's a cause and effect that we can pull from physical world or physical bodies that you can test the algorithms against as well. And then from the business side of the house, there's lots of ways to do it. Now, one of the things I like to correct on this is it's like, we often say the mantra of our company is we're about being directionally correct, not specifically wrong.
Speaker 1:Okay.
Speaker 6:And so when you need to make decisions, a lot of time when we do these simulations, we're like, here's the top 20% of things you need to absolutely keep doing because they knock it out of the park. Here's the 20% of things that are just literally hurting you.
Speaker 2:Okay.
Speaker 6:And when you have zero information, like you're in a pitch black room
Speaker 2:Mhmm.
Speaker 6:And you have no idea where the exits are, Would you rather take a really, really good educated guess about where the exit is, or would you rather wander around the dark?
Speaker 2:Yeah.
Speaker 6:And so I think that one of the things about business intelligence is when you have zero information, the value of the information you can get is that much more Yeah.
Speaker 2:Yeah. Important. Yeah.
Speaker 6:And so we do it
Speaker 2:very fast. Confused about the the back testing thing. Like, if I like, how would you go back and and assess the value of, like, Budweiser sponsoring Super Bowl forty or something? It's like you can't you can't run the counterfactual.
Speaker 6:Well,
Speaker 2:But you can simulate
Speaker 6:the simulations are assumptions. So think about it like this. A lot of people talk about probabilistic graph, right, graph analysis. Mhmm. Probabilistic graphs, whether they're causal, whether they're anything else
Speaker 2:Sure.
Speaker 6:Is to a certain extent, I mean, you know, Benendic, LLMs are almost a probabilistic graph. Sure. Right? Undirected till you query it.
Speaker 2:A neural network.
Speaker 6:And exactly. And so when we do this type of analysis, the thing about it is is that we actually have seen lots of instances of the same exact thing. Mhmm. It's about the specificity. So we just did a calculation literally yesterday that we looked at 1,000,000,000,000 connections across six months for a company.
Speaker 2:Mhmm.
Speaker 6:Like, that's the type of scale of analysis we're doing. We haven't seen when you haven't when you say you the counterfactual when you're saying, What does it look like when you have a base state? Mhmm. We've seen the operating version of a company over years. Mhmm.
Speaker 6:And so we know what the base state looks like when there's no influence from that. Mhmm. The key is you have to have enough data. Mhmm. So in the old world, when we're doing all this analysis, we used to say you hear about the term overfitting, right?
Speaker 6:Everybody's worried that is the model just biased? Well, the old world, you'd say, I wanna reduce the number of features, reduce the dimensionality to prevent overfitting. Right? To prevent over prediction. Well, in the modern world with, like, computational statistics nowadays, or AI as we call it, you want more features.
Speaker 6:Mhmm. You get more accurate the more data you have. That's counterintuitive for how people think about these type things. So my answer to you is we have to have immense amounts of data, right? And so we ingest a lot of it.
Speaker 6:And then that gives us enough vision, right, that we can see what a state would be and what would not be. And then we also provide our customers with confidence and everything else. So there are times where we really, really know, right? Yeah. And we go, this we have a 100% bet on, we see enough examples of this.
Speaker 6:Yeah. And sometimes we go, you're asking for a call on this and, yeah, we've got a pretty good guess, but you should So keep an
Speaker 8:interesting thing is is, you know, he's going fishing
Speaker 2:Sure.
Speaker 8:To see where, you know, the best fish are.
Speaker 2:Sure.
Speaker 8:And interestingly, we did one of these POCs for a very, very big branded company. And they were looking for the positive impact of event driven Sure. Brand marketing they were doing.
Speaker 2:Yeah.
Speaker 8:When they went out and sucked in all of this data Mhmm. To do this assessment for them, in addition to finding what were the sort of positive impacts of this, which were modest Mhmm. What they actually caught in the net was, unknown to them, a promotion being done literally in Canada by a, you know, a little subsidiary
Speaker 2:Interesting.
Speaker 8:That was just, you know, like a a a Yeah. A regional commercial Yeah. Which somehow or another bled over into The States. So it was being run-in Canada by a subsidiary there, bled over into The States, and had a tremendous negative adverse impact on brand.
Speaker 1:Yeah. Those Canadians.
Speaker 2:Mean, this is like like yeah. The cost example is like social media manager intern of your Canadian, you know, offshoot is doing something that goes viral in America and everyone
Speaker 6:funnier. They bought out a national the national sports, national hockey league final spot. Okay. And so you that's gonna be huge in Canada. Yeah.
Speaker 6:So they but that broadcast across the entirety of North America.
Speaker 4:Okay. So everyone saw it.
Speaker 6:Yeah. And so, like, suddenly this little, like, thing there.
Speaker 8:Drippy, ugly hamburger in the hand. It it's sure maybe natively they thought it was funny.
Speaker 2:Okay. Interesting. Interesting. And so you're looking at like when when sales data is happening relative to when the campaign goes on and then you tease out from there.
Speaker 6:Yeah. Think about it like, know, one of the big inspiration for the company was Renaissance Technologies out
Speaker 2:of New York. Sure.
Speaker 6:The high frequency trading firms and everything. Yeah. They can do a pretty good job about knowing when things affect each other or not. Mhmm. But you have to have an immense amount of incredibly high speed data sets.
Speaker 2:Talk about Accenture. You're partnering with them. Are they are they just an investor or are they also a go to market channel?
Speaker 6:Oh, well, they actually started as, one of these, tests we did because they
Speaker 2:were curious. They were a customer. Yeah.
Speaker 6:And the interesting
Speaker 8:thing Which is the best way when you think about it, John. So they started as a customer, then said, wait a minute. Yeah. We we have a multi multi billion dollar Mhmm. Business Yeah.
Speaker 8:Around marketing go to market.
Speaker 2:It seems like a lot of people go to Accenture for these questions. Julius. In Bain, NBCG, And
Speaker 8:so so then it went to, hey, can we help take you to market? Yeah. Which they've been fantastic at.
Speaker 2:I can imagine.
Speaker 8:And then that led to when this when Tomas doing this most recent round
Speaker 2:Yeah.
Speaker 8:Them stepping in is the biggest venture investor they've ever done.
Speaker 1:Wow. That's amazing. So big companies, Fortune 500, have been hiring Accenture and other consulting firms and research firms to help them understand what is driving results positive and negative in their business for a long time. What's the timeline to productizing what you're doing to a degree that a a much smaller company, let's say a company with like a million dollar a year advertising budget can actually start to get value out of this?
Speaker 2:Oh, that's interesting.
Speaker 6:Oh, so this is actually one of my favorite questions, because it has to with the law of long term vision of the company. One of the problems you get when you have midsize or small sized firms, and they're near and dear to my heart, is that they literally have never done things. So when you have a really large corporation
Speaker 1:Mhmm.
Speaker 6:Right, they've been in a podcast no matter they wanna be to or not, somebody's mentioned them. Right? All of these data sets across everything. But when you're a smaller company, right, you say a million dollar a year business or something like that, you haven't done everything. So there's no actual priors.
Speaker 6:There's no data. We don't know how they react. But eventually, when we see enough of the universe. Mhmm. Right?
Speaker 6:Just like you hear about world models, just like you hear about anything else, we'll actually know what the causal universe looks like. What is the actual most likely outcome of when somebody does something? Yeah. So in the future, you know, you know, say, a couple years, we'll actually be able to build synthetic data sets that you can send us any query, and we can respond to you what the most likely likely outcome would be. And where this gets really, really important is I think that, especially when you're doing private businesses, the world of LLMs and everything nowadays, I think they're amazing, but they're quickly converging.
Speaker 6:Right?
Speaker 2:Mhmm.
Speaker 6:There's not gonna be that much difference for a client between like Chattypete and Claude.
Speaker 2:Mhmm.
Speaker 6:Problem is that if you're using that for business intelligence and business decisions, what are you gonna do when your competitor gets the exact same answer and strategy you do? Mhmm. That's a real problem. And so we believe that we will take the best private data sets in the world, do stuff for just them, get the get our overall learnings in other places. And then we can actually provide people with strategies that are unique to them.
Speaker 2:Yeah.
Speaker 6:Right? Augment the other sets of intelligence. But Yeah.
Speaker 2:There are certain brands will get a better return on investment from being in the Super Bowl than others. Yeah. If you are a no name brand and you just put up a thirty second spot in the Super Bowl, people are gonna be like, I I Like your web server might crash.
Speaker 6:Right? Like all sorts
Speaker 2:of things. Yeah. Yeah. Your web server might you might not be ready for it, but also people might just be like, I'm not ready to learn about that as opposed to, I actually gonna I'm in the market for beer right now and thanks for showing me those Clydesdales. That makes sense.
Speaker 6:To kind of like talk to your question, like, that is an absolute dream that I have. Mhmm. Right? Being able to level the playing field across that but also advantages Yeah.
Speaker 1:Of these massive businesses. They can spend $20,000,000 to figure out what's really working and then do a lot more of that. Whereas a small firm is like kinda doing the vibes based analysis. Yeah. Started my career helping companies like advertise on YouTube channels and with podcasts and, you know, they might run a $200,000 campaign and there's some like direct attribution that they get either from a landing page or a code.
Speaker 1:Then they're like, wait, our conversion rate is just going up on the site generally. Totally. Is that being driven by changes that we made at the site level Yep. Changes that we made to the offer? Is it just overall lift from from podcast advertising or is it some other strategy entirely?
Speaker 1:And so the the the more you can bring like real business intelligence to small companies, the more they they'll be able to actually compete against the the big guys.
Speaker 6:And that makes me happy because everybody should have a little playing field and whoever has the best strategy and product should do well. Mhmm. Right? I think that it's important to note, as we kinda talk about this thing, we spent years years building the signal processor for this thing. Mhmm.
Speaker 6:We had to figure out how to bring in all this unstructured and semi structured data and be able to basically do Datadog for unstructured data Mhmm. First, before we could even try the causal thing.
Speaker 2:Mhmm.
Speaker 6:And so we have years of working on that. And that ingestion pipeline, that that skill there is what allows us to do what you're talking about. You can't just be like, I'm gonna slap a model on top of it. Right? You actually have to be able to have a sensor that can actually understand every data feed.
Speaker 1:Mhmm. Have you found a company yet that Jeffrey can't get an intro to or directly connect you to
Speaker 4:the CEO? Like this guy
Speaker 2:Imagine this.
Speaker 4:Oh, you wanna meet this guy? Yeah. I'll give him a call right now. Here here
Speaker 6:If you ever wanna not take a bet on something, that's one of the things I would not take a bet on. Right?
Speaker 1:Jeffrey will find them.
Speaker 8:Yes. Yes. Yes. Hunt you down.
Speaker 2:Well well, congratulations on the progress. Thank you so much for coming by the Ultra Dome. And, yeah. Good luck with the next with the next phase, putting the capital to work. It's gonna be an exciting time and I'm excited to hear more of these case studies as they roll out.
Speaker 6:Yeah. I really appreciate it both of
Speaker 1:you. Yeah. Rick, get let us know when you're ready for your first podcast customer. We got
Speaker 2:Yeah. Yeah. We gotta figure
Speaker 1:analysis that we wanna do. I mean, we we just do the we do the vibe space analysis.
Speaker 2:Did a billboard. Did a billboard in New York.
Speaker 8:In in the house.
Speaker 2:We did it really well.
Speaker 6:We should do it for
Speaker 2:We should just do
Speaker 6:it for fun anyways. Like, there's nothing more that I like than, like, looking at data sets.
Speaker 2:Yeah. Yeah. We ran we ran a billboard board campaign in in Manhattan. We ran two exactly two billboards. We have no idea whether or not it worked.
Speaker 2:It seemed to work because people shared it a lot on social media.
Speaker 8:For a million dollars, he could tell you. That
Speaker 2:well, we'll need to bargain that. For you all,
Speaker 6:we could do some drinks.
Speaker 2:It'll be fine. Well, thanks so much for coming on the show.
Speaker 4:Great to
Speaker 2:see you. Congratulations on all the progress. And we will go back to our regular scheduled programming. And I will tell you about ADDIO, customer relationship magic, the AI native CRM that builds scales and grows your company to the next level. Buco capital bloke is also black pilling on the timeline.
Speaker 2:There it is a bloodbath in the markets. Nvidia is down 4%.
Speaker 1:Microsoft's Michael Burry rage quit.
Speaker 2:Michael Burry rage quit. We need to talk about Michael Burry. I'm sure there's something in the stack. Let's go through some quick updates as we run through the show. Oh, this is cool.
Speaker 2:This is a white pill. Google d. Mine. SEMA two, our most capable AI agent for virtual three d worlds. Tyler, what's the deal with SEMA two?
Speaker 4:Yeah. This is really cool. So this is a it's like a general model that that can basically play, like, any video game Sure. Which is different because, like, you've seen a lot of, like, even early OpenAI, there was, like, Dota two.
Speaker 2:This is amazing for me because I don't have any time to play any video games anymore since I have kids. And if with this agent, I could just tell it to go play the game, and then I I could just Go
Speaker 1:have fun. Then Tell me then describe the fun you had.
Speaker 2:Yeah. And then summarize it.
Speaker 1:I'll get to fully
Speaker 2:experience to me, and I'll have Nick read the email and summarize that to me in a text message. That would be my experience of the video game. No. Seriously. What what I actually want this for I hate how modern video games, it takes twenty minutes to set up.
Speaker 2:Have you have you ever experienced this? It's like
Speaker 4:going through the tutorial?
Speaker 2:Remember when I Yeah. All I made you play Halo. When I made you play Halo, it it it took, like, ten minutes for you to actually get into the game, and then the actual game took you five minutes to play. And so, I like, there are a lot of times when I hear a new game, I'm like, I only have twenty minutes in my weekend to play this game. I wanna jump straight into the action.
Speaker 2:I don't want any of the opening unskippable cut scenes. I don't want any of the of the tutorial learn how to jump, learn how to crouch. I already know how to move. I know what the stick does. Don't tell me that.
Speaker 2:This is gonna solve that for me, hopefully.
Speaker 4:Yeah. But this is actually Yes. I think this is, like, one of the most interesting papers this year. Yes. A a big a big part of it is it's general, so it it you can put in a new game and then it does, like, self play essentially Sure.
Speaker 4:Which is, like, really important because that's, like, it's teaching itself. Yeah. This is basically the first, like, agentic model that can, like, see a new completely new environment
Speaker 2:Yeah.
Speaker 4:And then do self play Yeah. Where it gets better. So you
Speaker 2:I wonder how wild I I'd love to know how how diverse the inputs are. Like, does it expect an Xbox controller's worth of inputs? Does it expect a a keyboard's worth of inputs because that's more inputs that it would need to learn. That's fascinating. And then I wonder what happens if you start marrying it to generative world models in the future as those become games.
Speaker 2:Like, you have this weird, like, agent on simulated world. You're simulating both of these.
Speaker 4:That's a very interesting plan. A big part of it is is they connected it to Genie three.
Speaker 2:They did?
Speaker 4:Yeah. No way. It it works. So you could you have a a generative, basically, video game
Speaker 2:Okay.
Speaker 4:Where it's, like, generating frame by frame and it
Speaker 2:has all
Speaker 4:the cool and stuff. And then you have the generative agent that, like, learns what the the world is and then actually plays it. So you can see this, like, flywheel kind of starting very
Speaker 2:well. Whether you're going long or short, go to public.cominvesting for those to take it seriously. They got multi asset investing, industry leading yields, and they're trusted by millions.
Speaker 1:They also acquired Alto
Speaker 2:Oh, yeah.
Speaker 1:Big news. IRA, Crypto IRA. Yes. Yes. Yes.
Speaker 1:You're now gonna be able to hold digital assets in your retirement accounts all on public. Yes. They acquired Alto for $65,000,000 that got announced this morning. So great pickup from the public team. Major white pill, Miramaradi startup Thinking Machine Labs is in early talks.
Speaker 1:We love early talks. We love
Speaker 2:early talks.
Speaker 1:Some of our favorite talks to raise a new round of funding at evaluation of roughly 50,000,000,000 more 50 than billion. Four x their valuation for
Speaker 2:Everything's cooking. Everything's Stop
Speaker 8:black pilling.
Speaker 1:In other news, Anthropic disrupted a highly sophisticated AI led espionage campaign. The attack targeted large tech companies, financial institutions, chemical manufacturing companies, and government agencies. We assessed with high confidence that the threat actor was a Chinese state sponsored group. I guess they were using Claude, I I think is
Speaker 4:Yes. I I I think they were using Claude code actually.
Speaker 2:Weird.
Speaker 1:They said they were vibe coding espionage.
Speaker 4:Yeah. Yeah. You it's it was pretty funny. I I read through some of the blog post and it was like some of the the interactions of like the hackers and it was like, this is what they were saying to them all. They're like, okay.
Speaker 4:Good job, Claude, but I think this part is wrong. You can see, like, the actual transcript. Very bullish for anthropic.
Speaker 2:Well, go to 8sleep.com. Get a pod five. Five year warranty, thirty day risk free trial, free returns, free shipping. Michael Burry appears to be shutting down Scion Asset Management. He said, dear investors, with a heavy heart, I will liquidate the funds and return capital, but for a small audit slash tax holdback by year's end.
Speaker 2:My estimation of value in securities is not now and has not been for some time in sync with the markets. With heart with heartfelt thanks, but also with apologies, I wish you well in your future investments. I do suggest investors contact my associate PM.
Speaker 1:Did he really did he really quit right before the market started correcting? He's is this one of those, like, you know, 9090% quit right be ninety percent of gamblers quit right
Speaker 2:before Apparently, this is
Speaker 1:they finally called the top correctly.
Speaker 2:Yeah. It does seem odd. I mean, if he if he if there is a crash and he was going to be short, but he pulls out before, like, getting that short thesis to work and realizing the results of that, it really changes his legacy. It changes the meaning of that meme. It changes the the the meaning of the the Michael Burry image, in my opinion.
Speaker 2:But we'll see. I mean, he he might have he might be out of step for years. And we might look back on this and say that it was great that he got out. And it was great that he didn't he didn't short the greatest market.
Speaker 1:His memory will live on Yes. Through meme, images from the Big Short.
Speaker 2:And through whatever he gets on his wrist. Go to getbezel.com. Your bezel concierge is available now to source you any watch on the planet. Seriously, any watch.
Speaker 1:In other news, Paramount, Comcast, and Netflix are preparing bids for Warner Bros Discovery. They will have until November 20 to submit nonbinding first round bids.
Speaker 2:Yeah.
Speaker 1:Warner Warner Discovery is holding the auction process in the hopes of having it completed by the end of the year.
Speaker 2:Have you seen how how expensive streaming is getting? This is on the cover of the business and finance section in The Wall Street Journal. The price has gone up pretty much everything. Netflix has gone from something like $5 to 25 at the top end. Everyone's raising the price, and now they're creating bundles of streaming properties.
Speaker 1:It's only gonna be $2,000
Speaker 2:a month. They're all gonna have, yeah, $2,000 a month. They all have ads, constant
Speaker 1:all have different logins.
Speaker 2:All different logins, and, you can barely rebundle them even if you try.
Speaker 1:I wish I wish that we could get Jeffrey in here to talk about
Speaker 2:Yeah.
Speaker 1:This Warner Bros deal, but you're probably too close to the metal on this one to to provide to be able to really comment on it.
Speaker 2:Well, we'll tell you about Wander instead. Find your happy place. Book a Wander with inspiring reviews. Hotel grade amenities, dreamy beds, top tier cleaning, and twenty four seven concierge service. It's a vacation home,
Speaker 1:but better. In more news Please. Apparently, Vine is being rebooted
Speaker 2:No way.
Speaker 1:Under the name Divine with funding from Twitter's former CEO Jack Dorsey.
Speaker 2:Oh.
Speaker 1:The app's plans to feature more than 10,000 previously archived Vines and does not allow AI generated content.
Speaker 2:That's remarkable. There have been so many Vine revival attempts. Elon was talking about bringing it back One more time. Point. I believe the founder of Vine, was talking about bringing it back and and and and did a number of different projects.
Speaker 2:There was a project called v two, right, at some point. It would be fun. I I was a huge fan of Vine when it came out. I I I really enjoyed it as a new creative medium. It was it was very, very interesting, very, very fun.
Speaker 2:Let me tell you about adquick.com. Out of home advertising made easy and measurable. Say goodbye to the headaches of out of home advertising. Only ad quick combines technology, out of home expertise, and data to enable efficient seamless ad buying across the globe. What else?
Speaker 1:In other news, Strategy has gone below one NAV for the first time ever. Woah. Is Meaning that Sailors BTC Holdings are worse are worth less than their than their total debt. How is that how is that even possible? That seems very concerning.
Speaker 1:Anyways, their debt has long maturities, 2027 to 2032. Yeah. Yeah. They're not margin loans. But eventually, they'll be forced to sell if they can't make interest payments.
Speaker 1:Yeah. They have a something like a $700,000,000 worth of interest payments due next year, and they have, I think, under 50,000,000 of cash on hand at the moment. So Yeah. They'll either need to raise more or start selling.
Speaker 2:And and just, like, immense pressure from the other products in the market. I feel like, if you want access to Bitcoin, which has been Michael Saylor's flagship asset, you were able to initially mine it for free, which was weird, then then buy it on Coinbase with, a somewhat clunky process. Now it's pretty simple. Now you can buy Bitcoin on a credit card. You can get it in almost every app.
Speaker 2:You can you can get it in an ETF. You can get it in your in your retirement fund. There are a whole bunch of different ways to get exposure. That particular strategy is not the only one in town. Anything else, Jordy?
Speaker 2:Or should we wind down for the day and say goodbye to everyone? Say leave us five stars on Apple Podcasts
Speaker 1:Everybody, go DM the White House now Yes. On x and just request the hundred year mortgage. We need some type of bullish announcement.
Speaker 2:Yes.
Speaker 1:Otherwise, tomorrow will likely be even worse.
Speaker 2:Yes. But Well, if you do DM, x has changed the way DMs work. Now, there's unified DMs and encrypted chats all in one place. That is the last piece of news of the day. Well, thank you for tuning in.
Speaker 2:Leave us five stars on Apple Podcasts and Spotify, and we will see you tomorrow. Can't wait. AM Pacific. Sure.
Speaker 1:Goodbye. Cheers.